Awesome FL
v2024-beta
Tabla de contenido
Usamos otro proyecto para rastrear automáticamente las actualizaciones de los documentos FL; haga clic en FL-paper-update-tracker si lo necesita.
Se agregarán más elementos al repositorio . No dude en sugerir otros recursos clave abriendo un informe de problema, enviando una solicitud de extracción o enviándome un correo electrónico @ ([email protected]). Si desea comunicarse con más amigos en el campo del aprendizaje federado, únase al grupo QQ [联邦学习交流群], el número del grupo es 833638275. ¡Disfrute leyendo!
Aviso de actualización del repositorio
2024/09/30
Estimados usuarios, Nos gustaría informarles sobre algunos cambios que afectarán a este repositorio de código abierto. ¿El propietario y colaborador principal @ youngfish42 ha completado con éxito sus estudios de doctorado? al 30 de septiembre de 2024 y desde entonces ha cambiado su enfoque de investigación. Este cambio de circunstancias afectará la frecuencia y el alcance de las actualizaciones de la lista de documentos del repositorio.
En lugar de las actualizaciones periódicas anteriores, anticipamos que la lista impresa ahora se actualizará mensual o trimestralmente. Además, se reducirá la profundidad de estas actualizaciones. Por ejemplo, las actualizaciones relacionadas con la institución del autor y el código fuente abierto ya no se mantendrán activamente.
Entendemos que esto podría afectar el valor que usted obtiene de este repositorio. Por lo tanto, invitamos humildemente a más contribuyentes a participar en la actualización del contenido. Este esfuerzo colaborativo garantizará que el repositorio siga siendo un recurso valioso para todos.
Agradecemos su comprensión y esperamos su continuo apoyo y contribuciones.
Atentamente,
白小鱼 (pez joven)
categorias
Inteligencia Artificial (IJCAI, AAAI, AISTATS, ALT, AI)
Aprendizaje automático (NeurIPS, ICML, ICLR, COLT, UAI, Aprendizaje automático, JMLR, TPAMI)
Minería de datos (KDD, WSDM)
Seguro (S&P, CCS, Seguridad USENIX, NDSS)
Visión por computadora (ICCV, CVPR, ECCV, MM, IJCV)
Procesamiento del lenguaje natural (ACL, EMNLP, NAACL, COLING)
Recuperación de información (SIGIR)
Base de datos (SIGMOD, ICDE, VLDB)
Red (SIGCOMM, INFOCOM, MOBICOM, NSDI, WWW)
Sistema (OSDI, SOSP, ISCA, MLSys, EuroSys, TPDS, DAC, TOCS, TOS, TCAD, TC)
Otros (CISE, FOCS, STOC)
| Evento | 2024-2020 | antes de 2020 |
|---|---|---|
| IJCAI | 24, 23, 22, 21, 20 | 19 |
| AAAI | 24, 23, 22, 21, 20 | - |
| AISTATAS | 24, 23, 22, 21, 20 | - |
| ALTA | 22 | - |
| IA (J) | 23 | - |
| NeurIPS | 24, 23, 22, 21, 20 | 18, 17 |
| ICML | 24, 23, 22, 21, 20 | 19 |
| ICLR | 24, 23, 22, 21, 20 | - |
| POTRO | 23 | - |
| AUI | 23, 22, 21 | - |
| Aprendizaje automático (J) | 24, 23, 22 | - |
| JMLR (J) | 24, 23, 22 | - |
| TPAMI (J) | 25, 24, 23, 22 | - |
| KDD | 24, 23, 22, 21, 20 | |
| WSDM | 24, 23, 22, 21 | 19 |
| S&P | 24, 23, 22 | 19 |
| CCS | 24, 23, 22, 21, 19 | 17 |
| Seguridad USENIX | 23, 22, 20 | - |
| NDSS | 24, 23, 22, 21 | - |
| CVPR | 24, 23, 22, 21 | - |
| ICCV | 23,21 | - |
| ECV | 24, 22, 20 | - |
| MM | 24, 23, 22, 21, 20 | - |
| IJCV (J) | 24 | - |
| LCA | 23, 22, 21 | 19 |
| NAACL | 24, 22, 21 | - |
| EMNLP | 24, 23, 22, 21, 20 | - |
| COLANDO | 20 | - |
| SIGIR | 24, 23, 22, 21, 20 | - |
| SIGMOD | 22, 21 | - |
| ICDE | 24, 23, 22, 21 | - |
| VLDB | 23, 22, 21, 21, 20 | - |
| SIGCOMM | - | - |
| INFOCOM | 24, 23, 22, 21, 20 | 19, 18 |
| Mobicom | 24, 23, 22, 21, 20 | |
| INDE | 23(1, 2) | - |
| www | 24, 23, 22, 21 | |
| OSDI | 21 | - |
| SOSP | 21 | - |
| ISCA | 24 | - |
| MLSys | 24, 23, 22, 20 | 19 |
| EuroSys | 24, 23, 22, 21, 20 | |
| TPDS (J) | 24, 23, 22, 21, 20 | - |
| CAD | 24, 22, 21 | - |
| TOCS | - | - |
| Términos de servicio | - | - |
| CAD | 24, 23, 22, 21 | - |
| TC | 24, 23, 22, 21 | - |
| CISE | 23, 21 | - |
| FOCS | - | - |
| STOC | - | - |
palabras clave
Estadísticas: código disponible y estrellas >= 100 | cita >= 50 | ? Lugar de primer nivel
kg. : Gráfico de conocimiento | data. : conjunto de datos | surv. : encuesta
Los artículos de aprendizaje federado en Nature (y sus subrevistas), Cell, Science (y Science Advances) y PANS se refieren al motor de búsqueda WOS.
| Título | Afiliación | Evento | Año | Materiales |
|---|---|---|---|---|
| MatSwarm: cálculo confiable de materiales impulsados por el aprendizaje de transferencia de enjambres para compartir big data de forma segura | USTB; UNT | Nat. Comunitario. | 2024 | [PUB] [CÓDIGO] |
| Introducción de inteligencia de punta a los medidores inteligentes a través del aprendizaje dividido federado | HKU | Nat. Comunitario. | 2024 | [PUB] [新闻] |
| Un estudio internacional que presenta una plataforma de inteligencia artificial de aprendizaje federado para tumores cerebrales pediátricos | Universidad Stanford | Nat. Comunitario. | 2024 | [PUB] [CÓDIGO] |
| PPML-Omics: un método de aprendizaje automático federado que preserva la privacidad protege la privacidad de los pacientes en datos ómicos | KAUST | Avances científicos | 2024 | [PUB] [CÓDIGO] |
| El aprendizaje federado no es una panacea para la ética de los datos | TUM; UVA | Nat. Mach. Intel.(Comentario) | 2024 | [PUB] |
| Modelo de aprendizaje sólidamente federado para identificar pacientes de alto riesgo con recurrencia posoperatoria del cáncer gástrico | Hospital Central de Jiangmen; Universidad de Tecnología Aeroespacial de Guilin; Universidad de Tecnología Electrónica de Guilin; | Nat. Comunitario. | 2024 | [PUB] [CÓDIGO] |
| Intercambio selectivo de conocimientos para una destilación federada que preserve la privacidad sin un buen profesor | HKUST | Nat. Comunitario. | 2024 | [PUB] [PDF] [CÓDIGO] |
| Un sistema de aprendizaje federado para oncología de precisión en Europa: DigiONE | IQVIA Investigación del Cáncer BV | Nat. Medicina. (Comentario) | 2024 | [PUB] |
| Computación cuántica ciega distribuida multicliente con la arquitectura Qline | Universidad Sapienza de Roma | Nat. Comunitario. | 2023 | [PUB] [PDF] |
| Aleatoriedad cuántica independiente del dispositivo: prueba de conocimiento cero mejorada | USTC | PNAS | 2023 | [PUB] [PDF] [新闻] |
| Clasificación colaborativa y que preserva la privacidad de baterías retiradas para un reciclaje directo rentable mediante aprendizaje automático federado | Universidad de Tsinghua | Nat. Comunitario. | 2023 | [PUB] |
| Abogando por la privacidad de los neurodatos y la regulación de la neurotecnología | Universidad de Columbia | Nat. Protocolo. (Perspectiva) | 2023 | [PUB] |
| Evaluación comparativa federada de inteligencia artificial médica con MedPerf | IHU Estrasburgo; Universidad de Estrasburgo; Instituto del Cáncer Dana-Farber; Medicina Weill Cornell; Escuela de Salud Pública TH Chan de Harvard; MIT; Intel | Nat. Mach. Intel. | 2023 | [PUB] [PDF] [CÓDIGO] |
| Equidad algorítmica en inteligencia artificial para la medicina y la atención sanitaria | Escuela de Medicina de Harvard; Broad Institute de Harvard y el Instituto de Tecnología de Massachusetts; Instituto del Cáncer Dana-Farber | Nat. Biomédica. Ing. (Perspectiva) | 2023 | [PUB] [PDF] |
| Transferencia de conocimiento diferencialmente privada para el aprendizaje federado | JUE | Nat. Comunitario. | 2023 | [PUB] [CÓDIGO] |
| Aprendizaje federado descentralizado mediante el intercambio de modelos proxy | IA de capa 6; Universidad de Waterloo; Instituto de vectores | Nat. Comunitario. | 2023 | [PUB] [PDF] [CÓDIGO] |
| Aprendizaje automático federado en investigaciones que cumplen con las normas de protección de datos | Universidad de Hamburgo | Nat. Mach. Intel.(Comentario) | 2023 | [PUB] |
| Aprendizaje federado para predecir la respuesta histológica a la quimioterapia neoadyuvante en cáncer de mama triple negativo | owkin | Nat. Medicina. | 2023 | [PUB] [CÓDIGO] |
| El aprendizaje federado permite big data para la detección de límites de cánceres raros | Universidad de Pensilvania | Nat. Comunitario. | 2022 | [PUB] [PDF] [CÓDIGO] |
| Aprendizaje federado y soberanía de datos genómicos indígenas | abrazando la cara | Nat. Mach. Intel. (Comentario) | 2022 | [PUB] |
| Aprendizaje federado de representación desenredada para la detección de anomalías cerebrales no supervisadas | tum | Nat. Mach. Intel. | 2022 | [PUB] [PDF] [CÓDIGO] |
| Cambiar el aprendizaje automático para la atención sanitaria del desarrollo a la implementación y de los modelos a los datos | Universidad de Stanford; Biociencias de piedra verde | Nat. Biomédica. Ing. (Artículo de revisión) | 2022 | [PUB] |
| Un marco de red neuronal de gráficos federados para una personalización que preserva la privacidad | JUE | Nat. Comunitario. | 2022 | [PUB] [CÓDIGO] [解读] |
| Aprendizaje federado eficiente en comunicación a través de la destilación del conocimiento | JUE | Nat. Comunitario. | 2022 | [PUB] [PDF] [CÓDIGO] |
| Liderar el aprendizaje neuromórfico federado para la inteligencia artificial de vanguardia inalámbrica | XMU; UNT | Nat. Comunitario. | 2022 | [PUB] [CÓDIGO] [解读] |
| Un novedoso enfoque de aprendizaje federado descentralizado para capacitar sobre datos médicos privados protegidos, de mala calidad y distribuidos globalmente | Universidad de Wollongong | Ciencia. Reps. | 2022 | [PUB] |
| Avanzando en el diagnóstico de COVID-19 con una colaboración en inteligencia artificial que preserve la privacidad | FOTOS | Nat. Mach. Intel. | 2021 | [PUB] [PDF] [CÓDIGO] |
| Aprendizaje federado para predecir resultados clínicos en pacientes con COVID-19 | Radiología del MGH y Escuela de Medicina de Harvard | Nat. Medicina. | 2021 | [PUB] [CÓDIGO] |
| Interferencia adversaria y sus mitigaciones en el aprendizaje automático colaborativo que preserva la privacidad | Colegio Imperial de Londres; TUM; Minado abierto | Nat. Mach. Inteligencia (perspectiva) | 2021 | [PUB] |
| Swarm Learning para el aprendizaje automático clínico descentralizado y confidencial | DZNE; Universidad de Bonn; | Naturaleza ? | 2021 | [PUB] [CÓDIGO] [SOFTWARE] [解读] |
| Privacidad de extremo a extremo que preserva el aprendizaje profundo en imágenes médicas multiinstitucionales | TUM; Colegio Imperial de Londres; Minado abierto | Nat. Mach. Intel. | 2021 | [PUB] [CÓDIGO] [解读] |
| Aprendizaje federado eficiente en comunicación | CUHK; Universidad de Princeton | SARTENES. | 2021 | [PUB] [CÓDIGO] |
| Romper los límites del intercambio de datos médicos mediante el uso de radiografías sintetizadas | Universidad RWTH de Aquisgrán | Ciencia. Insinuaciones. | 2020 | [PUB] [CÓDIGO] |
| Aprendizaje automático federado, seguro y que preserva la privacidad en imágenes médicas | TUM; Colegio Imperial de Londres; Minado abierto | Nat. Mach. Inteligencia (perspectiva) | 2020 | [PUB] |
Artículos de aprendizaje federado aceptados por las principales conferencias y revistas de IA (Inteligencia artificial), incluida IJCAI (Conferencia internacional conjunta sobre inteligencia artificial), AAAI (Conferencia AAAI sobre inteligencia artificial), AISTATS (Inteligencia artificial y estadística), ALT (Conferencia internacional sobre aprendizaje algorítmico) Teoría), IA (Inteligencia Artificial).
| Título | Afiliación | Evento | Año | Materiales |
|---|---|---|---|---|
| Agrupación federada de vistas múltiples mediante factorización tensorial | IJCAI | 2024 | [PUB] | |
| Agrupación multivista federada eficiente con factorización matricial integrada y K-Means | IJCAI | 2024 | [PUB] | |
| LG-FGAD: un marco eficaz de detección de anomalías de gráficos federados | IJCAI | 2024 | [PUB] | |
| Aprendizaje rápido federado para modelos Weather Foundation en dispositivos | IJCAI | 2024 | [PUB] | |
| Rompiendo las barreras de la heterogeneidad del sistema: aprendizaje federado multimodal tolerante a los rezagados mediante la destilación del conocimiento | IJCAI | 2024 | [PUB] | |
| Desaprendizaje durante el aprendizaje: un método eficiente de desaprendizaje de máquinas federadas | IJCAI | 2024 | [PUB] | |
| Compresión de gradiente híbrida práctica para sistemas de aprendizaje federados | IJCAI | 2024 | [PUB] | |
| Descubrimiento causal federado consciente de la heterogeneidad de la calidad de la muestra mediante la selección adaptativa del espacio variable | IJCAI | 2024 | [PUB] [CÓDIGO] | |
| Aprendizaje federado regularizado con normas de funciones: utilización de disparidades de datos para mejorar el rendimiento del modelo | IJCAI | 2024 | [PUB] [CÓDIGO] | |
| Cuantificación de la incertidumbre basada en Dirichlet para el aprendizaje federado personalizado con redes posteriores mejoradas | IJCAI | 2024 | [PUB] | |
| FedConPE: bandidos conversacionales federados eficientes con clientes heterogéneos | IJCAI | 2024 | [PUB] | |
| DarkFed: un ataque de puerta trasera sin datos en el aprendizaje federado | IJCAI | 2024 | [PUB] | |
| Desaprendizaje federado escalable mediante fragmentación aislada y codificada | IJCAI | 2024 | [PUB] | |
| Mejora de la recomendación entre dominios de doble objetivo con aprendizaje federado que preserva la privacidad | IJCAI | 2024 | [PUB] | |
| Fuga de etiquetas en el aprendizaje federado vertical: una encuesta | IJCAI | 2024 | [PUB] | |
| El auge de la inteligencia federada: de los modelos de fundaciones federadas a la inteligencia colectiva | IJCAI | 2024 | [PUB] | |
| LEAP: Optimización del aprendizaje federado jerárquico en datos que no son IID con el juego de formación de coaliciones | IJCAI | 2024 | [PUB] | |
| EAB-FL: Exacerbando el sesgo algorítmico mediante ataques de envenenamiento de modelos en el aprendizaje federado | IJCAI | 2024 | [PUB] | |
| Destilación del conocimiento en el aprendizaje federado: una guía práctica | IJCAI | 2024 | [PUB] | |
| FedGCS: un marco generativo para la selección eficiente de clientes en el aprendizaje federado mediante optimización basada en gradientes | IJCAI | 2024 | [PUB] | |
| FedPFT: ajuste fino de proxy federado de modelos de base | IJCAI | 2024 | [PUB] [CÓDIGO] | |
| Una encuesta sistemática sobre el aprendizaje semisupervisado federado | IJCAI | 2024 | [PUB] | |
| Agentes inteligentes para el aprendizaje federado basado en subastas: una encuesta | IJCAI | 2024 | [PUB] | |
| Una estrategia de oferta libre de sesgos para maximizar los ingresos para los consumidores de datos en el aprendizaje federado basado en subastas | IJCAI | 2024 | [PUB] | |
| Aprendizaje federado personalizado basado en calibración dual | IJCAI | 2024 | [PUB] | |
| Apoyo a las decisiones orientado a las partes interesadas para el aprendizaje federado basado en subastas | IJCAI | 2024 | [PUB] | |
| Redefiniendo las contribuciones: aprendizaje federado impulsado por Shapley | IJCAI | 2024 | [PUB] [CÓDIGO] | |
| Una encuesta sobre métodos eficientes de aprendizaje federado para la formación del modelo básico | IJCAI | 2024 | [PUB] | |
| De la optimización a la generalización: aprendizaje federado justo frente al cambio de calidad mediante la coincidencia de nitidez entre clientes | IJCAI | 2024 | [PUB] [CÓDIGO] | |
| FBLG: un enfoque basado en gráficos locales para manejar datos no IID con asimetría dual en el aprendizaje federado | IJCAI | 2024 | [PUB] | |
| FedFa: un paradigma de formación totalmente asincrónico para el aprendizaje federado | IJCAI | 2024 | [PUB] | |
| FedSSA: Agregación basada en similitudes semánticas para un aprendizaje federado personalizado, heterogéneo y con modelos eficientes | IJCAI | 2024 | [PUB] | |
| FedES: Detención anticipada federada para dificultar la memorización de ruidos de etiquetas heterogéneos | IJCAI | 2024 | [PUB] | |
| Aprendizaje federado personalizado para la predicción del tráfico entre ciudades | IJCAI | 2024 | [PUB] | |
| Adaptación federada para recomendaciones basadas en modelos básicos | IJCAI | 2024 | [PUB] | |
| BADFSS: ataques de puerta trasera al aprendizaje autosupervisado federado | IJCAI | 2024 | [PUB] | |
| Estimación antes de eliminar el sesgo: un enfoque bayesiano para separar el sesgo previo en el aprendizaje semisupervisado federado | IJCAI | 2024 | [PUB] [CÓDIGO] | |
| FedTAD: destilación de conocimientos sin datos y consciente de la topología para el aprendizaje federado de subgrafos | IJCAI | 2024 | [PUB] | |
| BOBA: Aprendizaje federado bizantino robusto con asimetría de etiquetas | UIUC | AISTATAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| Bandidos contextuales lineales federados con clientes heterogéneos | Universidad de Virginia | AISTATAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| Diseño de experimentos federados bajo privacidad diferencial distribuida | Universidad de Stanford; Meta | AISTATAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| Escapar de los puntos de silla en el aprendizaje federado heterogéneo a través de SGD distribuido con compresión de comunicación | Universidad de Princeton | AISTATAS | 2024 | [PUB] [PDF] |
| SGD asincrónico en gráficos: un marco unificado para la optimización asincrónica descentralizada y federada | INRIA | AISTATAS | 2024 | [PUB] [PDF] |
| SIFU: desaprendizaje federado informado secuencial para un desaprendizaje de clientes eficiente y demostrable en optimización federada | INRIA | AISTATAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| Compresión con distribución exacta de errores para aprendizaje federado | Escuela Politécnica | AISTATAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| Optimización Minimax federada adaptativa con menores complejidades | Universidad de Nueva Jersey; Laboratorio clave del MIIT de análisis de patrones e inteligencia artificial | AISTATAS | 2024 | [PUB] [PDF] |
| Compresión adaptativa en el aprendizaje federado a través de información secundaria | Universidad de Stanford; Universidad de Padua | AISTATAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| Aprendizaje federado bajo demanda para distribuciones arbitrarias de clases objetivo | UNISTA | AISTATAS | 2024 | [PUB] [CÓDIGO] |
| FedFisher: Aprovechamiento de la información de Fisher para el aprendizaje federado de una sola vez | UMC | AISTATAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| Dinámica de colas del aprendizaje federado asincrónico | Huawei | AISTATAS | 2024 | [PUB] [PDF] |
| Bandido armado en X federado personalizado | Universidad Purdue | AISTATAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| Aprendizaje federado para registros médicos electrónicos heterogéneos utilizando redes de atención de gráficos temporales aumentadas | Universidad de Oxford | AISTATAS | 2024 | [PUB] [CÓDIGO] |
| Ascenso de descenso de gradiente suavizado estocástico para optimización minimax federada | Universidad de Virginia | AISTATAS | 2024 | [PUB] [PDF] |
| Comprender la generalización del aprendizaje federado a través de la estabilidad: la heterogeneidad importa | Universidad del Noroeste | AISTATAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| Beneficios mutuos demostrables del aprendizaje federado en dominios sensibles a la privacidad | Universidad de Sofía | AISTATAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| Análisis de fugas de privacidad en modelos de lenguaje grande federados | universidad de florida | AISTATAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| Agregador invariante para la defensa contra ataques de puerta trasera federados | UIUC | AISTATAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| Aprendizaje federado con comunicación eficiente con datos y heterogeneidad de clientes | ISTA | AISTATAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| FedMut: aprendizaje federado generalizado mediante mutación estocástica | UNT | AAAI | 2024 | [PUB] |
| Aprendizaje federado de etiquetas parciales con regularización y aumento adaptativo local | Universidad de Carleton | AAAI | 2024 | [PUB] [PÁGINA] |
| ¡Sin prejuicios! Redes neuronales de gráficos federados justos para recomendación personalizada | IIT | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| La lógica formal permitió el aprendizaje federado personalizado mediante la inferencia de propiedades | Universidad de Vanderbilt | AAAI | 2024 | [PUB] [PDF] |
| Aprendizaje de representación independiente de tareas que preserva la privacidad para el aprendizaje federado contra ataques de inferencia de atributos | Tecnología de Illinois | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Comercio justo: lograr compensaciones óptimas de Pareto entre precisión equilibrada y equidad en el aprendizaje federado | Universidad Leibniz | AAAI | 2024 | [PUB] [PÁGINA] |
| Combatir los desequilibrios de datos en el aprendizaje semisupervisado federado con reguladores duales | HKUST | AAAI | 2024 | [PUB] [PÁGINA] [PDF] |
| Fed-QSSL: un marco para el aprendizaje federado personalizado con ancho de bits y heterogeneidad de datos | Utah | AAAI | 2024 | [PUB] [PÁGINA] [PDF] |
| Sobre la separación de la transferencia asimétrica de conocimientos para el aprendizaje federado independiente de la modalidad y la tarea | Universidad de Virginia | AAAI | 2024 | [PUB] |
| FedDAT: un enfoque para el ajuste del modelo básico en el aprendizaje federado heterogéneo multimodal | LMU Múnich Siemens AG | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Cuide su cabeza: ensamblaje de cabezales de proyección para preservar la confiabilidad de los modelos federados | Laboratorio clave conjunto de inteligencia artificial de Shaanxi de la Universidad Xi'an Jiaotong | AAAI | 2024 | [PUB] [PÁGINA] [PDF] |
| FedGCR: lograr rendimiento y equidad para el aprendizaje federado con distintos tipos de clientes mediante la personalización y reponderación del grupo | UNT | AAAI | 2024 | [PUB] [PÁGINA] [CÓDIGO] |
| Codificadores federados de modalidad específica y anclajes multimodales para la segmentación personalizada de tumores cerebrales | Universidad de Xiamen | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Explotación de sesgos de etiquetas en el aprendizaje federado con concatenación de modelos | NUES | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Destilación de conocimientos complementarios para un modelo sólido y que preserve la privacidad que sirva en el aprendizaje federado vertical | SUSTO; HKUST | AAAI | 2024 | [PUB] [PÁGINA] |
| Aprendizaje federado mediante destilación colaborativa de entrada y salida | Universidad de Búfalo; Estados Unidos Escuela de Medicina de Harvard | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Aprendizaje federado de una ronda calibrado con inferencia bayesiana en el espacio predictivo | Instituto de vectores de la Universidad de Waterloo | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| FedCSL: un enfoque escalable y preciso para el aprendizaje de estructuras causales federadas | HFUT | AAAI | 2024 | [PUB] [PDF] |
| FedFixer: Mitigar el ruido heterogéneo de las etiquetas en el aprendizaje federado | Universidad Xi'an Jiaotong; Universidad de Leiden | AAAI | 2024 | [PUB] [PÁGINA] [PDF] |
| FedLPS: aprendizaje federado heterogéneo para múltiples tareas con uso compartido de parámetros locales | Universidad de Nueva Jersey | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Aprendizaje de tres niveles federado demostrablemente convergente | TJU | AAAI | 2024 | [PUB] [PDF] |
| Aprendizaje federado performativo: una solución a los cambios de distribución heterogéneos y dependientes del modelo | MU | AAAI | 2024 | [PUB] [PÁGINA] |
| Inteligencia de comercio general: motor basado en PNL federado globalmente para servicios personalizados sostenibles y que preservan la privacidad de múltiples comerciantes | Universidad Kyung Hee; Harex InfoTech | AAAI | 2024 | [PUB] [PÁGINA] |
| EMGAN: Early-Mix-GAN sobre la extracción del modelo del lado del servidor en el aprendizaje federado dividido | IA de Sony | AAAI | 2024 | [PUB] [PÁGINA] [CÓDIGO] |
| FedDiv: filtrado colaborativo de ruido para el aprendizaje federado con etiquetas ruidosas | SYSU; HKU | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Transformador de puntos con aprendizaje federado para predecir el estado de HER2 del cáncer de mama a partir de imágenes de portaobjetos completos teñidas con hematoxilina y eosina | USTC; CAS | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| FedNS: un algoritmo de tipo Newton de boceto rápido para el aprendizaje federado | CAS | AAAI | 2024 | [PUB] [PDF] [CÓDIGO] |
| Bandido federado armado en X | Universidad Purdue | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Fundación algorítmica del aprendizaje federado con datos secuenciales | GMU | AAAI | 2024 | [PUB] |
| UFDA: Adaptación universal del dominio federado con supuestos prácticos | XJTU; Universidad de Sídney | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| FedASMU: Aprendizaje federado asincrónico eficiente con actualización dinámica del modelo consciente del estancamiento | Hithink RoyalFlush Información Red Co | AAAI | 2024 | [PUB] [PÁGINA] [PDF] |
| Transformador guiado por lenguaje para clasificación federada de etiquetas múltiples | UNT | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| FedCD: aprendizaje semisupervisado federado con equilibrio de conciencia de clase a través de profesores duales | SZU | AAAI | 2024 | [PUB] [PÁGINA] [CÓDIGO] |
| Más allá de las amenazas tradicionales: un ataque persistente de puerta trasera al aprendizaje federado | UME | AAAI | 2024 | [PUB] [PÁGINA] [CÓDIGO] |
| Aprendizaje federado con clientes extremadamente ruidosos mediante destilación negativa | XMU | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| FedST: Aprendizaje de transferencia de estilo federado para segmentación de imágenes no IID | USTB | AAAI | 2024 | [PUB] [PÁGINA] [学报] [CÓDIGO] |
| PPIDSG: un esquema de intercambio de imágenes que preserva la privacidad con GAN en el aprendizaje federado | USTC | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Un marco de gemelo digital cognitivo (CDT) basado en el aprendizaje federado (PPFL) para preservar la privacidad para ciudades inteligentes | DCU | AAAI | 2024 | [PUB] |
| Un algoritmo primario-dual para el aprendizaje federado híbrido | Universidad del Noroeste | AAAI | 2024 | [PUB] [PÁGINA] [PDF] |
| FedLF: Aprendizaje federado justo por capas | CUHK; Instituto Shenzhen de Inteligencia Artificial y Robótica para la Sociedad | AAAI | 2024 | [PUB] [PÁGINA] |
| Hacia el aprendizaje federado de Fair Graph a través de mecanismos de incentivos | ZJU; FDU | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Hacia la solidez del aprendizaje federado diferencialmente privado | JUE | AAAI | 2024 | [PUB] [PÁGINA] |
| Resistir ataques de puerta trasera en el aprendizaje federado mediante elecciones bidireccionales y perspectiva individual | ZJU; HUAWEI | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| El número entero es suficiente: cuando el aprendizaje federado vertical se encuentra con el redondeo | ZJU; grupo de hormigas | AAAI | 2024 | [PUB] [PÁGINA] |
| Aprendizaje federado guiado por CLIP sobre heterogeneidad y datos de cola larga | XMU | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Ajuste rápido adaptativo federado para el aprendizaje colaborativo multidominio | FDU | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Aprendizaje federado justo multidimensional | SDU | AAAI | 2024 | [PUB] [PÁGINA] [PDF] |
| HiFi-Gas: Mecanismo de incentivos de aprendizaje federado jerárquico Estimación mejorada del uso de gas | Grupo ENN | AAAI | 2024 | [PUB] |
| Sobre el papel del impulso del servidor en el aprendizaje federado | Universidad de Virginia | AAAI | 2024 | [PUB] [PDF] |
| FedCompetitors: Colaboración armoniosa en el aprendizaje federado con participantes competidores | BUPT | AAAI | 2024 | [PUB] [PÁGINA] [PDF] |
| z-SignFedAvg: una compresión estocástica unificada basada en signos para el aprendizaje federado | CUHK; Instituto de Investigación de Big Data de Shenzhen de China | AAAI | 2024 | [PUB] [PÁGINA] [PDF] |
| Aprendizaje federado asincrónico consciente de la disparidad de datos y la indisponibilidad temporal para el mantenimiento predictivo de flotas de transporte | Grupo Volkswagen | AAAI | 2024 | [PUB] [PÁGINA] |
| Aprendizaje de gráficos federados bajo cambio de dominio con prototipos generalizables | ¿QUÉ? | AAAI | 2024 | [PUB] [PÁGINA] |
| TurboSVM-FL: Impulsar el aprendizaje federado mediante la agregación SVM para clientes diferidos | Universidad Técnica de Munich | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Minimización de discrepancia de gradiente colaborativa de múltiples fuentes para la generalización de dominios federados | TJU | AAAI | 2024 | [PUB] [PDF] [CÓDIGO] |
| Ocultación de muestras sensibles contra fugas de gradiente en el aprendizaje federado | Universidad de Monash | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| FedA3I: Agregación consciente de la calidad de las anotaciones para la segmentación federada de imágenes médicas frente al ruido de anotaciones heterogéneo | FOTOS | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Aprendizaje de causalidad federada con optimización adaptativa explicable | SDU | AAAI | 2024 | [PUB] [PÁGINA] [PDF] |
| Bandidos en cascada contextuales federados con comunicación asincrónica y usuarios heterogéneos | USTC | AAAI | 2024 | [PUB] [PÁGINA] [PDF] |
| Exploración del aprendizaje federado semisupervisado de una sola vez con modelos de difusión previamente entrenados | FDU | AAAI | 2024 | [PUB] [PDF] |
| Estilización co-restringida por diversidad y autenticidad para la generalización de dominios federados en la reidentificación de personas | XMU; Universidad de Trento | AAAI | 2024 | [PUB] [PÁGINA] |
| PerFedRLNAS: búsqueda personalizada de arquitectura neuronal federada, única para todos | U de T | AAAI | 2024 | [PUB] [PÁGINA] |
| Aprendizaje federado asincrónico eficiente con agregación de impulso prospectivo y corrección detallada | BUPT | AAAI | 2024 | [PUB] [PÁGINA] |
| Ataques adversarios a algoritmos de velocidad de bits adaptativos aprendidos federados | HKU | AAAI | 2024 | [PUB] |
| FedTGP: Prototipos globales entrenables con aprendizaje contrastivo mejorado con margen adaptativo para datos y heterogeneidad de modelos en el aprendizaje federado | SJTU | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| LR-XFL: Aprendizaje federado explicable basado en el razonamiento lógico | UNT | AAAI | 2024 | [PUB] [PDF] [CÓDIGO] |
| Un enfoque de minimización de pérdidas de Huber para el aprendizaje federado robusto bizantino | Laboratorio de Zhejiang | AAAI | 2024 | [PUB] [PÁGINA] [PDF] |
| Entrenamiento de parámetros consciente del conocimiento para un aprendizaje federado personalizado | Universidad del Noreste | AAAI | 2024 | [PUB] [PÁGINA] |
| Aprendizaje federado de ruido de etiquetas con regularización de productos de diversidad local | SJTU | AAAI | 2024 | [PUB] [PÁGINA] [SUPP] |
| Agregación ponderada adaptada en el aprendizaje federado (resumen del estudiante) | UBC | AAAI | 2024 | [PUB] |
| Transferencia de conocimientos mediante un modelo compacto en el aprendizaje federado (resumen del estudiante) | Universidad de Sídney | AAAI | 2024 | [PUB] [PÁGINA] |
| PICSR: enrutador entre silos basado en prototipos para aprendizaje federado (resumen del estudiante) | Laboratorio de Auton de la Universidad Estatal de Ohio, CMU | AAAI | 2024 | [PUB] [PÁGINA] |
| Red de convolución de gráficos que preserva la privacidad para la recomendación de elementos federados | SZU | AI | 2023 | [PUB] |
| Ganar-ganar: un marco federado que preserva la privacidad para recomendaciones entre dominios de doble objetivo | CAS; UCAS; Tecnología JD; Investigación JD sobre Ciudades Inteligentes | AAAI | 2023 | [PUB] |
| Ataque no dirigido contra sistemas de recomendación federados mediante incrustaciones de elementos venenosos y la defensa | USTC; Laboratorio estatal clave de inteligencia cognitiva | AAAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| Crowdsourcing federado impulsado por incentivos | SDU | AAAI | 2023 | [PUB] [PDF] |
| Abordar la heterogeneidad de datos en el aprendizaje federado con prototipos de clases | Universidad de Lehigh | AAAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| FairFed: Habilitar la equidad grupal en el aprendizaje federado | USC | AAAI | 2023 | [PUB] [PDF] [解读] |
| Propagación de la robustez federada: compartir la robustez adversa en el aprendizaje federado heterogéneo | Universidad Estatal de Michigan | AAAI | 2023 | [PUB] |
| Esparsificación del complemento: poda de modelos con bajos gastos generales para el aprendizaje federado | NJIT | AAAI | 2023 | [PUB] |
| Comunicación casi gratuita en la identificación federada del mejor brazo | NUES | AAAI | 2023 | [PUB] [PDF] |
| Agregación de modelos adaptativos por capas para un aprendizaje federado escalable | Universidad del Sur de California Universidad Inha | AAAI | 2023 | [PUB] [PDF] |
| Envenenamiento con Cerberus: ataque de puerta trasera sigiloso y coludido contra el aprendizaje federado | BJTU | AAAI | 2023 | [PUB] |
| FedMDFG: Aprendizaje federado con descenso multigradiente y orientación justa | CUHK; El Instituto Shenzhen de Inteligencia Artificial y Robótica para la Sociedad | AAAI | 2023 | [PUB] |
| Garantizar la agregación segura: mitigar la fuga de privacidad de múltiples rondas en el aprendizaje federado | USC | AAAI | 2023 | [PUB] [PDF] [VÍDEO] [CÓDIGO] |
| Aprendizaje federado en gráficos no IID mediante el intercambio de conocimientos estructurales | UTS | AAAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| Identificación eficiente de similitudes de distribución en el aprendizaje federado agrupado a través de ángulos principales entre subespacios de datos del cliente | UCSD | AAAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| FedABC: Apuntando a la competencia justa en el aprendizaje federado personalizado | QUÉ; Laboratorio Hubei Luojia; Academia JD Explore | AAAI | 2023 | [PUB] [PDF] |
| Más allá de ADMM: un marco de aprendizaje federado adaptativo unificado y con variación reducida del cliente | SUTD | AAAI | 2023 | [PUB] [PDF] |
| FedGS: muestreo federado basado en gráficos con disponibilidad arbitraria de clientes | XMU | AAAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| Aprendizaje federado adaptativo más rápido | universidad de pittsburgh | AAAI | 2023 | [PUB] [PDF] |
| FedNP: hacia el aprendizaje federado no IID mediante la propagación neuronal federada | HKUST | AAAI | 2023 | [PUB] [CÓDIGO] [VIDEO] [SUPP] |
| Coincidencia neuronal federada bayesiana que completa la información completa | TJU | AAAI | 2023 | [PUB] [PDF] |
| CDMA: un algoritmo práctico de aprendizaje federado entre dispositivos para problemas generales de Minimax | ZJU | AAAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| Modelo generativo federado sobre datos heterogéneos de múltiples fuentes en IoT | GSU | AAAI | 2023 | [PUB] |
| DeFL: Defensa contra ataques de envenenamiento de modelos en el aprendizaje federado a través de la concientización sobre los períodos críticos de aprendizaje | SUNY-Universidad de Binghamton | AAAI | 2023 | [PUB] |
| FedALA: Agregación local adaptativa para el aprendizaje federado personalizado | SJTU | AAAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| Profundizando en la solidez adversa del aprendizaje federado | ZJU | AAAI | 2023 | [PUB] [PDF] |
| Sobre la vulnerabilidad de las defensas de puerta trasera para el aprendizaje federado | TJU | AAAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| Echo of Neighbors: amplificación de la privacidad para el aprendizaje federado privado personalizado con modelo aleatorio | RUC; Centro de Investigación en Ingeniería del Ministerio de Educación sobre Bases de Datos y BI | AAAI | 2023 | [PUB] [PDF] |
| DPAUC: Computación AUC diferencialmente privada en aprendizaje federado | ByteDance Inc. | Pistas especiales AAAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| Capacitación eficiente de modelos de diagnóstico de fallas industriales a gran escala mediante abandono de bloques oportunistas federados | UNT | Programas especiales AAAI | 2023 | [PUB] [PDF] |
| Aprendizaje federado orquestado a escala industrial para el descubrimiento de fármacos | KU Lovaina | Programas especiales AAAI | 2023 | [PUB] [PDF] [VÍDEO] |
| Una herramienta federada de seguimiento del aprendizaje para la simulación de vehículos autónomos (resumen del estudiante) | CNU | Programas especiales AAAI | 2023 | [PUB] |
| MGIA: Ataque de inversión de gradiente mutuo en el aprendizaje federado multimodal (resumen del estudiante) | PoliU | Programas especiales AAAI | 2023 | [PUB] |
| Aprendizaje federado agrupado para datos heterogéneos (resumen del estudiante) | RUC | Programas especiales AAAI | 2023 | [PUB] |
| FedSampling: una mejor estrategia de muestreo para el aprendizaje federado | JUE | IJCAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| HyperFed: exploración de prototipos hiperbólicos con agregación consistente de datos no IID en aprendizaje federado | ZJU | IJCAI | 2023 | [PUB] [PDF] |
| FedOBD: abandono de bloques oportunista para entrenar de manera eficiente redes neuronales a gran escala mediante el aprendizaje federado | UNT | IJCAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| Modelado de distribución de preferencias probabilísticas federadas con agrupación conjunta de compacidad para recomendaciones multidominio que preservan la privacidad | ZJU | IJCAI | 2023 | [PUB] |
| Aprendizaje estructural y semántico de gráficos federados | ¿QUÉ? | IJCAI | 2023 | [PUB] |
| BARA: Mecanismo de incentivos eficiente con asignación de presupuesto de recompensas en línea en el aprendizaje federado entre silos | SYSU | IJCAI | 2023 | [PUB] [PDF] |
| FedDWA: aprendizaje federado personalizado con ajuste de peso dinámico | SYSU | IJCAI | 2023 | [PUB] [PDF] |
| FedPass: Aprendizaje profundo federado vertical que preserva la privacidad con ofuscación adaptativa | banco web | IJCAI | 2023 | [PUB] [PDF] |
| Codificador automático de gráficos federados globalmente consistente para gráficos no IID | FZU | IJCAI | 2023 | [PUB] [CÓDIGO] |
| Aprendizaje por refuerzo competitivo-cooperativo de múltiples agentes para el aprendizaje federado basado en subastas | UNT | IJCAI | 2023 | [PUB] |
| Personalización dual por recomendación federada | JLU; Universidad Tecnológica de Sídney | IJCAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| FedNoRo: Hacia un aprendizaje federado resistente al ruido abordando el desequilibrio de clases y la heterogeneidad del ruido de etiquetas | FOTOS | IJCAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| Denegación de servicio o control detallado: hacia un modelo flexible de ataques de envenenamiento al aprendizaje federado | Universidad de Xiangtan | IJCAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| FedHGN: un marco federado para redes neuronales de gráficos heterogéneos | CUHK | IJCAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| FedET: un marco de aprendizaje incremental de clases federado y eficiente en comunicación basado en Transformer mejorado | Ping una tecnología; JUE | IJCAI | 2023 | [PUB] [PDF] |
| Aprendizaje federado rápido para la predicción meteorológica: hacia modelos básicos sobre datos meteorológicos | UTS | IJCAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| FedBFPT: un marco de aprendizaje federado eficiente para la formación previa adicional de Bert | ZJU | IJCAI | 2023 | [PUB] [CÓDIGO] |
| Aprendizaje federado bayesiano: una encuesta | Seguimiento de la encuesta IJCAI | 2023 | [PDF] | |
| Una encuesta sobre evaluación federada en el aprendizaje federado | Universidad Macquarie | Seguimiento de la encuesta IJCAI | 2023 | [PUB] [PDF] |
| SAMBA: un marco genérico para bandidos federados seguros con múltiples armas (resumen ampliado) | Centro INSA Val de Loira | Seguimiento del diario IJCAI | 2023 | [PUB] |
| El costo de la comunicación de la seguridad y la privacidad en la estimación de frecuencia federada | stanford | AISTATAS | 2023 | [PUB] [CÓDIGO] |
| Aprendizaje federado eficiente y liviano mediante abandono distribuido asincrónico | Universidad de arroz | AISTATAS | 2023 | [PUB] [CÓDIGO] |
| Aprendizaje federado bajo la deriva del concepto distribuido | UMC | AISTATAS | 2023 | [PUB] [CÓDIGO] |
| Caracterización de los ataques de evasión interna en el aprendizaje federado | UMC | AISTATAS | 2023 | [PUB] [CÓDIGO] |
| Asintóticas federadas: un modelo para comparar algoritmos de aprendizaje federados | stanford | AISTATAS | 2023 | [PUB] [CÓDIGO] |
| Aprendizaje federado privado no convexo sin un servidor confiable | USC | AISTATAS | 2023 | [PUB] [CÓDIGO] |
| Aprendizaje federado para flujos de datos | Universidad de la Costa Azul | AISTATAS | 2023 | [PUB] [CÓDIGO] |
| Nada más que arrepentimientos: descubrimiento causal federado que preserva la privacidad | Centro Helmholtz para la seguridad de la información | AISTATAS | 2023 | [PUB] [CÓDIGO] |
| Ataque de inferencia de membresía activa bajo privacidad diferencial local en el aprendizaje federado | UFL | AISTATAS | 2023 | [PUB] [CÓDIGO] |
| Dinámica de Langevin de promedio federado: hacia una teoría unificada y nuevos algoritmos | CMAP | AISTATAS | 2023 | [PUB] |
| Aprendizaje federado robusto y bizantino con tasas estadísticas óptimas | Universidad de Berkeley | AISTATAS | 2023 | [PUB] [CÓDIGO] |
| Aprendizaje federado en gráficos no IID mediante el intercambio de conocimientos estructurales | UTS | AAAI | 2023 | [PDF] [CÓDIGO] |
| FedGS: muestreo federado basado en gráficos con disponibilidad arbitraria de clientes | XMU | AAAI | 2023 | [PDF] [CÓDIGO] |
| Crowdsourcing federado impulsado por incentivos | SDU | AAAI | 2023 | [PDF] |
| Hacia la comprensión de la selección sesgada de clientes en el aprendizaje federado. | UMC | AISTATAS | 2022 | [PUB] [CÓDIGO] |
| FLIX: una alternativa simple y eficiente en términos de comunicación a los métodos locales en el aprendizaje federado | KAUST | AISTATAS | 2022 | [PUB] [PDF] [CÓDIGO] |
| Límites definidos para el promedio federado (SGD local) y la perspectiva continua. | stanford | AISTATAS | 2022 | [PUB] [PDF] [CÓDIGO] |
| Aprendizaje por refuerzo federado con heterogeneidad ambiental. | PKU | AISTATAS | 2022 | [PUB] [PDF] [CÓDIGO] |
| Detección federada de comunidades miopes con comunicación única | Purdue | AISTATAS | 2022 | [PUB] [PDF] |
| Algoritmos asincrónicos de límite superior de confianza para bandidos lineales federados. | Universidad de Virginia | AISTATAS | 2022 | [PUB] [PDF] [CÓDIGO] |
| Hacia el aprendizaje de estructuras de redes bayesianas federadas con optimización continua. | UMC | AISTATAS | 2022 | [PUB] [PDF] [CÓDIGO] |
| Aprendizaje federado con agregación asincrónica almacenada en búfer | Meta IA | AISTATAS | 2022 | [PUB] [PDF] [VÍDEO] |
| Aprendizaje federado diferencialmente privado sobre datos heterogéneos. | stanford | AISTATAS | 2022 | [PUB] [PDF] [CÓDIGO] |
| SparseFed: Mitigar los ataques de envenenamiento de modelos en el aprendizaje federado con dispersión | Princeton | AISTATAS | 2022 | [PUB] [PDF] [CÓDIGO] [VÍDEO] |
| La base importa: métodos de segundo orden mejores y más eficientes en la comunicación para el aprendizaje federado | KAUST | AISTATAS | 2022 | [PUB] [PDF] |
| Impulso de gradiente funcional federado. | Universidad de Pensilvania | AISTATAS | 2022 | [PUB] [PDF] [CÓDIGO] |
| QLSD: Dinámica estocástica de Langevin cuantificada para el aprendizaje federado bayesiano. | Laboratorio de IA Criteo | AISTATAS | 2022 | [PUB] [PDF] [CÓDIGO] [VÍDEO] |
Extrapolación de conocimientos basada en metaaprendizaje para gráficos de conocimiento en el entorno federado kg. | ZJU | IJCAI | 2022 | [PUB] [PDF] [CÓDIGO] |
| Aprendizaje federado personalizado con un gráfico | UTS | IJCAI | 2022 | [PUB] [PDF] [CÓDIGO] |
| Red neuronal gráfica federada verticalmente para la clasificación de nodos que preservan la privacidad | ZJU | IJCAI | 2022 | [PUB] [PDF] |
| Adaptarse a la adaptación: personalización del aprendizaje para el aprendizaje federado entre silos | IJCAI | 2022 | [PUB] [PDF] [CÓDIGO] | |
| Transferencia de conocimiento del conjunto heterogéneo para capacitar modelos grandes en el aprendizaje federado | IJCAI | 2022 | [Pub] [PDF] | |
| Aprendizaje federado semi-supervisado privado. | IJCAI | 2022 | [PUB] | |
| Aprendizaje federado continuo basado en la destilación del conocimiento. | IJCAI | 2022 | [PUB] | |
| Aprendizaje federado sobre datos heterogéneos y de cola larga a través del re-entrenamiento del clasificador con características federadas | IJCAI | 2022 | [Pub] [PDF] [Código] | |
| Atención múltiple de la tarea federada para el reconocimiento de la actividad humana cruzada | IJCAI | 2022 | [PUB] | |
| Aprendizaje federado personalizado con generalización contextualizada. | IJCAI | 2022 | [Pub] [PDF] | |
| Aprendizaje federado de protección: agregación robusta con selección adaptativa del cliente. | IJCAI | 2022 | [Pub] [PDF] | |
| FedCG: Aproveche el GaN condicional para proteger la privacidad y mantener el rendimiento competitivo en el aprendizaje federado | IJCAI | 2022 | [Pub] [PDF] [Código] | |
| FedDuap: aprendizaje federado con actualización dinámica y poda adaptativa utilizando datos compartidos en el servidor. | IJCAI | 2022 | [Pub] [PDF] | |
Hacia el aprendizaje federado verificable surv. | IJCAI | 2022 | [Pub] [PDF] | |
| Harmofl: Armonizar las derivaciones locales y globales en el aprendizaje federado en imágenes médicas heterogéneas | Cuhk; BUAA | AAAI | 2022 | [Pub] [pdf] [código] [解读] |
| Aprendizaje federado para el reconocimiento facial con corrección de gradiente | Bucear | AAAI | 2022 | [Pub] [PDF] |
| Spreadgnn: aprendizaje federado de tareas múltiples descentralizado para redes neuronales gráficas en datos moleculares | USC | AAAI | 2022 | [Pub] [pdf] [código] [解读] |
| SmartIDX: Reducción del costo de comunicación en el aprendizaje federado al explotar las estructuras CNNS | GOLPEAR; PCL | AAAI | 2022 | [Pub] [código] |
| Punrones entre señales de procesamiento cognitivo y características lingüísticas a través de una red atencional unificada | TJU | AAAI | 2022 | [Pub] [PDF] |
| Aprovechar períodos de aprendizaje críticos en el aprendizaje federado | Universidad de SUNY-Binghamton | AAAI | 2022 | [Pub] [PDF] |
| Coordinando Momenta para el aprendizaje federado de Silo cruzado | universidad de pittsburgh | AAAI | 2022 | [Pub] [PDF] |
| FedProto: aprendizaje prototipo federado sobre dispositivos heterogéneos | UTS | AAAI | 2022 | [Pub] [PDF] [Código] |
| Fedsoft: aprendizaje federado en clúster suave con actualización local proximal | CMU | AAAI | 2022 | [Pub] [PDF] [Código] |
| Entrenamiento disperso dinámico federado: calcular menos, comunicarse menos, pero aprendiendo mejor | La Universidad de Texas en Austin | AAAI | 2022 | [Pub] [PDF] [Código] |
| FedFR: marco federado de optimización conjunta para reconocimiento facial genérico y personalizado | Universidad Nacional de Taiwán | AAAI | 2022 | [Pub] [PDF] [Código] |
| Splitfed: cuando el aprendizaje federado se encuentra con el aprendizaje dividido | Csiro | AAAI | 2022 | [Pub] [PDF] [Código] |
| Programación de dispositivos eficientes con aprendizaje federado de múltiples trabajo | Universidad de SoOCHOW | AAAI | 2022 | [Pub] [PDF] |
| Alineación de gradiente implícito en el aprendizaje distribuido y federado | Iit kanpur | AAAI | 2022 | [Pub] [PDF] |
| Clasificación de vecinos más cercanos federados con una colonia de moscas de frutas | Investigación de IBM | AAAI | 2022 | [Pub] [PDF] [Código] |
| Campos vectores y conservadurismo itérados, con aplicaciones al aprendizaje federado. | ALTA | 2022 | [Pub] [PDF] | |
| Aprendizaje federado con privacidad amplificada por dispersión y optimización adaptativa | IJCAI | 2021 | [Pub] [PDF] [Video] | |
| Distribución de comportamiento imitan: combinación de comportamientos individuales y grupales para el aprendizaje federado | IJCAI | 2021 | [Pub] [PDF] | |
| Fedspeech: texto a voz federada con aprendizaje continuo | IJCAI | 2021 | [Pub] [PDF] | |
| Aprendizaje federado práctico de un solo disparo para un entorno cruzado | IJCAI | 2021 | [Pub] [PDF] [Código] | |
| Destilación del modelo federado con privacidad diferencial sin ruido | IJCAI | 2021 | [Pub] [PDF] [Video] | |
| LDP-FL: agregación privada práctica en el aprendizaje federado con privacidad diferencial local | IJCAI | 2021 | [Pub] [PDF] | |
| Aprendizaje federado con promedio justo. | IJCAI | 2021 | [Pub] [PDF] [Código] | |
| H-FL: una arquitectura jerárquica de comunicación eficiente y protegida por la privacidad para el aprendizaje federado. | IJCAI | 2021 | [Pub] [PDF] | |
| Aprendizaje de borde federal descentralizado de comunicación y escalable. | IJCAI | 2021 | [PUB] | |
| Aprendizaje federado vertical asíncrono seguro con la actualización hacia atrás | Universidad Xidian; JD Tech | AAAI | 2021 | [Pub] [PDF] [Video] |
| FedRec ++: Recomendación federada sin pérdida con comentarios explícitos | Szu | AAAI | 2021 | [Pub] [video] |
| Bandidos múltiples federados | Universidad de Virginia | AAAI | 2021 | [Pub] [PDF] [Código] [Video] |
| Sobre la convergencia del SGD local eficiente en comunicación para el aprendizaje federado | Universidad del Templo; universidad de pittsburgh | AAAI | 2021 | [Pub] [video] |
| Flame: aprendizaje federado diferencialmente privado en el modelo Shuffle | Universidad Renmin de China; Universidad de Kyoto | AAAI | 2021 | [Pub] [PDF] [Video] [Código] |
| Hacia la comprensión de la influencia de los clientes individuales en el aprendizaje federado | Sjtu; La Universidad de Texas en Dallas | AAAI | 2021 | [Pub] [PDF] [Video] |
| Aprendizaje federado seguro de manera probable contra clientes maliciosos | Universidad de Duke | AAAI | 2021 | [Pub] [PDF] [video] [diapositiva] |
| Aprendizaje federado personalizado de Silo en datos no IID | Universidad Simon Fraser; Universidad McMaster | AAAI | 2021 | [Pub] [PDF] [Video] [UC.] |
| Juegos para compartir modelos: analizar el aprendizaje federado bajo participación voluntaria | Universidad de Cornell | AAAI | 2021 | [Pub] [PDF] [Código] [Video] |
| Maldición o redención? Cómo la heterogeneidad de los datos afecta la robustez del aprendizaje federado | Universidad de Nevada; Investigación de IBM | AAAI | 2021 | [Pub] [PDF] [Video] |
| Juego de gradientes: mitigando clientes irrelevantes en el aprendizaje federado | IIT Bombay; Investigación de IBM | AAAI | 2021 | [Pub] [PDF] [Código] [Video] [Supp] |
| Esquema de descenso de coordenadas de bloques federados para aprender modelos globales y personalizados | Cuhk; Universidad Estatal de Arizona | AAAI | 2021 | [Pub] [PDF] [Video] [Código] |
| Abordar el desequilibrio de clases en el aprendizaje federado | Universidad del Noroeste | AAAI | 2021 | [Pub] [pdf] [video] [código] [解读] |
| Defender contra la puertas traseras en el aprendizaje federado con una tasa de aprendizaje sólida | La Universidad de Texas en Dallas | AAAI | 2021 | [Pub] [PDF] [Video] [Código] |
| Ataques de corredor libre a la agregación de modelos en el aprendizaje federado | Laboratorios de Accenture | Aistats | 2021 | [Pub] [PDF] [Código] [Video] [Supp] |
| Privacidad F-Diferencial Federada | Universidad de Pensilvania | Aistats | 2021 | [Pub] [código] [video] [Supp] |
| Aprendizaje federado con compresión: análisis unificado y garantías nítidas | La Universidad Estatal de Pensilvania; La Universidad de Texas en Austin | Aistats | 2021 | [Pub] [PDF] [Código] [Video] [Supp] |
| Modelo barajado de privacidad diferencial en el aprendizaje federado | UCLA; Google | Aistats | 2021 | [Pub] [video] [Supp] |
| Compensaciones de convergencia y precisión en el aprendizaje federado y el meta-aprendizaje | Aistats | 2021 | [Pub] [PDF] [Video] [Supp] | |
| Bandidos múltiples federados con personalización | Universidad de Virginia; La Universidad Estatal de Pensilvania | Aistats | 2021 | [Pub] [PDF] [Código] [Video] [Supp] |
| Hacia la participación flexible del dispositivo en el aprendizaje federado | CMU; Sysu | Aistats | 2021 | [Pub] [PDF] [Video] [Supp] |
| Meta-aprendizaje federado para la detección de tarjetas de crédito fraudulentas | IJCAI | 2020 | [Pub] [video] | |
| Un juego de múltiples jugadores para estudiar esquemas de incentivos de aprendizaje federado | IJCAI | 2020 | [Pub] [código] [解读] | |
| Árboles de decisión de impulso de gradiente federado práctico | Nus; UWA | AAAI | 2020 | [Pub] [PDF] [Código] |
| Aprendizaje federado para problemas de fundación de visión y lengua | Pku; Tencent | AAAI | 2020 | [PUB] |
| Asignación de Dirichlet latente federado: un marco basado en la privacidad diferencial local | BUAA | AAAI | 2020 | [PUB] |
| Hashosto de paciente federado | Universidad de Cornell | AAAI | 2020 | [PUB] |
| Aprendizaje federado robusto a través de la enseñanza de máquinas colaborativas | Symantec Research Labs; Kausto | AAAI | 2020 | [Pub] [PDF] |
| FedVision: una plataforma de detección de objetos visuales en línea impulsada por el aprendizaje federado | Webank | AAAI | 2020 | [Pub] [PDF] [Código] |
| FedPAQ: un método de aprendizaje federado eficiente en comunicación con promedio periódico y cuantificación | UC Santa Bárbara; Ut Austin | Aistats | 2020 | [Pub] [PDF] [Video] [Supp] |
| Cómo hacer una puerta trasera al aprendizaje federado | Cornell Tech | Aistats | 2020 | [Pub] [PDF] [Video] [Código] [Supp] |
| Descubrimiento de los bateadores federados con privacidad diferencial | RPI; Google | Aistats | 2020 | [Pub] [PDF] [Video] [Supp] |
| Visualización de múltiples agentes para explicar el aprendizaje federado | Webank | IJCAI | 2019 | [Pub] [video] |
Documentos de aprendizaje federados aceptados por la conferencia y revista Top ML (Machine Learning), incluidas las neuripas (Conferencia Anual sobre Sistemas de Procesamiento de Información Neural), ICML (Conferencia Internacional sobre Aprendizaje Máquina), ICLR (Conferencia Internacional sobre Representaciones de Aprendizaje), Colt (Conferencia Anual Computacional Teoría del aprendizaje), UAI (Conferencia sobre incertidumbre en inteligencia artificial), Aprendizaje automático, JMLR (Journal of Machine Learning Research), TPAMI (IEEE Transacciones en análisis de patrones e inteligencia de máquinas).
| Título | Afiliación | Evento | Año | Materiales |
|---|---|---|---|---|
| Estabilización y aceleración del aprendizaje federado en datos heterogéneos con participación parcial del cliente | Tpami | 2025 | [PUB] | |
| Modelo federado médico con una mezcla de componentes personalizados y compartidos | Tpami | 2025 | [PUB] | |
| Aprendizaje federado de un solo disparo a través de la comunicación sintética del destilador del desanimado | Neuros | 2024 | [PUB] | |
| Aprendizaje federado no convexo en submanifolds suaves compactos con datos heterogéneos | Neuros | 2024 | [PUB] | |
| FedGMKD: un marco de aprendizaje federado prototipo eficiente a través de la destilación del conocimiento y la agregación consciente de la discrepancia | Neuros | 2024 | [PUB] | |
| Mejora de la generalización en el aprendizaje federado con regularización de información mutua de datos modelo: un enfoque de inferencia posterior | Neuros | 2024 | [PUB] | |
| Modelo federado Heterogéneo Matryoshka Representación de Representación Aprendizaje | Neuros | 2024 | [PUB] | |
| Aprendizaje gráfico federado para recomendación de dominio cruzado | Neuros | 2024 | [PUB] | |
| Fedgmark: marca de agua certificadamente robusta para el aprendizaje gráfico federado | Neuros | 2024 | [PUB] | |
| Adaptador de doble personalización para modelos de fundación federada | Neuros | 2024 | [PUB] | |
| Métodos de gradiente de política natural federada para el aprendizaje de refuerzo de múltiples tareas | Neuros | 2024 | [PUB] | |
| Domar la larga cola en la predicción de la movilidad humana | Neuros | 2024 | [PUB] | |
| Defensa dual: mejorar la privacidad y mitigar los ataques de envenenamiento en el aprendizaje federado | Neuros | 2024 | [PUB] | |
| Optimizadores mejorados por gráficos para la recomendación de la estructura de estructura incrustación de la evolución | Neuros | 2024 | [PUB] | |
| DOFIT: ajuste de instrucciones federadas por dominio con olvido catastrófico aliviado | Neuros | 2024 | [PUB] | |
| Aprendizaje federado eficiente contra la falta de disponibilidad de clientes heterogéneos y no estacionarios | Neuros | 2024 | [PUB] | |
| Transformador federado: aprendizaje federado vertical multipartidista en datos prácticos vinculados difusamente | Neuros | 2024 | [PUB] | |
| Fiarse: aprendizaje federado modelo heterogéneo a través de la extracción de submodelos consciente de la importancia | Neuros | 2024 | [PUB] | |
| Aviso federado probabilístico con datos no IID y desequilibrados | Neuros | 2024 | [PUB] | |
| Flora: modelos de idiomas grandes federados con adaptaciones heterogéneas de bajo rango | Neuros | 2024 | [PUB] | |
| Taming de varianza de representación de dominio cruzado en el aprendizaje prototipo federado con dominios de datos heterogéneos | Neuros | 2024 | [PUB] | |
| PfedClub: agregación de modelo heterogéneo controlable para el aprendizaje federado personalizado | Neuros | 2024 | [PUB] | |
| ¿Por qué ir lleno? Elevación del aprendizaje federado a través de actualizaciones de redes parciales | Neuros | 2024 | [PUB] | |
| FUSEFL: Aprendizaje federado de una sola vez a través de la lente de causalidad con fusión de modelo progresivo | Neuros | 2024 | [PUB] | |
| FEDSSP: aprendizaje gráfico federado con conocimiento espectral y preferencia personalizada | Neuros | 2024 | [PUB] | |
| Manejo de aprendizaje de los espacios de características heterogéneos con explicación de etiquetas explícitas | Neuros | 2024 | [PUB] | |
| A-FEDPD: Alinear las necesidades de aprendizaje primario dual federal es todas | Neuros | 2024 | [PUB] | |
| Estimación de frecuencia privada y personalizada en un entorno federado | Neuros | 2024 | [PUB] | |
| La compensación de complejidad de la comunicación de la muestra en Q-learning federado | Neuros | 2024 | [PUB] | |
| Aprendizaje de refuerzo fuera de línea dirigido por el conjunto federado | Neuros | 2024 | [PUB] | |
| Adaptación federada de caja negra para segmentación semántica | Neuros | 2024 | [PUB] | |
| Pensando hacia adelante: Fineting Federated Fineting de modelos de idiomas | Neuros | 2024 | [PUB] | |
| Aprendizaje federado de los modelos de fundación en lenguaje de visión: análisis teórico y método | Neuros | 2024 | [PUB] | |
| Diseño óptimo para la obtención de preferencias humanas | Neuros | 2024 | [PUB] | |
| Hacia diversos dispositivos de aprendizaje federado heterogéneo a través de la integración del conocimiento aritmético de la tarea | Neuros | 2024 | [PUB] | |
| Aprendizaje federado personalizado a través de la adaptación de distribución de características | Neuros | 2024 | [PUB] | |
| SCAFFLSA: Heterogeneidad de domesticación en aproximación estocástica lineal federada y aprendizaje de TD | Neuros | 2024 | [PUB] | |
| Un enfoque bayesiano para el aprendizaje federado personalizado en entornos heterogéneos | Neuros | 2024 | [PUB] | |
| RFLPA: un sólido marco de aprendizaje federado contra ataques de envenenamiento con agregación segura | Neuros | 2024 | [PUB] | |
| Fedgtst: Aumento de la transferibilidad global de los modelos federados a través de la afinación de estadísticas | Neuros | 2024 | [PUB] | |
| Agrupación de aprendizaje de extremo a extremo para el aprendizaje de la intención en recomendación | Neuros | 2024 | [PUB] | |
| FedLPA: aprendizaje federado de una sola vez con agregación posterior en capa | Neuros | 2024 | [PUB] | |
| TIME-FFM: Hacia el modelo de fundación federada con poder LM para el pronóstico de series de tiempo | Neuros | 2024 | [PUB] | |
| FOOGD: colaboración federada para generalización y detección fuera de distribución | Neuros | 2024 | [PUB] | |
| Una navaja suiza para el aprendizaje federado heterogéneo: acoplamiento flexible a través de la norma traza | Neuros | 2024 | [PUB] | |
| Fedne: Vecino federado asistido por sustitución incrustando para la reducción de la dimensionalidad | Neuros | 2024 | [PUB] | |
| La capacitación local de baja precisión es suficiente para el aprendizaje federado | Neuros | 2024 | [PUB] | |
| Aprendizaje auto-supervisado de los recursos conscientes de los recursos con representaciones de clase global | Neuros | 2024 | [PUB] | |
| Sobre la necesidad de colaboración para la selección de modelos en línea con datos descentralizados | Neuros | 2024 | [PUB] | |
| El poder de la extrapolación en el aprendizaje federado | Neuros | 2024 | [PUB] | |
| (Fl) $^2 $: Superar pocas etiquetas en el aprendizaje semi-supervisado federado | Neuros | 2024 | [PUB] | |
| Sobre las estrategias de muestreo para fragmentos de modelos espectrales | Neuros | 2024 | [PUB] | |
| Personalización de modelos de idiomas con lora en cuanto a instancias para recomendación secuencial | Neuros | 2024 | [PUB] | |
| SPAFL: aprendizaje federado eficiente en comunicación con modelos dispersos y baja sobrecarga computacional | Neuros | 2024 | [PUB] | |
| Hydra-FL: destilación de conocimiento híbrido para un aprendizaje federado robusto y preciso | Neuros | 2024 | [PUB] | |
| Métodos de punto proximal estabilizados para la optimización federada | Neuros | 2024 | [PUB] | |
| Dapperfl: aprendizaje federado adaptativo de dominio con poda de fusión modelo para dispositivos de borde | Neuros | 2024 | [PUB] | |
| Disección de disparidades de parámetros para defensa de puerta trasera en el aprendizaje federado heterogéneo | Neuros | 2024 | [PUB] | |
| ¿El agente de peor rendimiento lidera el paquete? Análisis de la dinámica del agente en SGD distribuido unificado | Neuros | 2024 | [PUB] | |
| FedAVP: aumentar los datos locales a través de la política compartida en el aprendizaje federado | Neuros | 2024 | [PUB] | |
| Cobo: aprendizaje colaborativo a través de la optimización bilevel | Neuros | 2024 | [PUB] | |
| Análisis de convergencia del aprendizaje federado dividido en datos heterogéneos | Neuros | 2024 | [PUB] | |
| Grupo federado eficiente en comunicación Optimización sólida distributionalmente | Neuros | 2024 | [PUB] | |
| Ferrari: desaprendizaje de características federadas mediante la optimización de la sensibilidad de las funciones | Neuros | 2024 | [PUB] | |
| Aprendizaje federado sobre modos conectados | Neuros | 2024 | [PUB] | |
| Aprendizaje federado personalizado con una mezcla de modelos para predicción adaptativa y modelos ajustados | Neuros | 2024 | [PUB] | |
| ¿La equidad igualitaria conduce a la inestabilidad? Los límites de la equidad en el aprendizaje federado estable bajo comportamientos altruistas | Neuros | 2024 | [PUB] | |
| Predicción en línea federada de expertos con privacidad diferencial: separaciones y aceleraciones de arrepentimiento | Neuros | 2024 | [PUB] | |
| DataStealing: robar datos de modelos de difusión en el aprendizaje federado con múltiples troyanos | Neuros | 2024 | [PUB] | |
| Aviones conductuales federados: explicar la evolución del comportamiento del cliente en el aprendizaje federado | Neuros | 2024 | [PUB] | |
| Aprendizaje federado jerárquico con corrección de gradiente de múltiples tiempos | Neuros | 2024 | [PUB] | |
| Hiperprismo: un marco de agregación no lineal adaptativo para el aprendizaje automático distribuido sobre datos no IID y enlaces de comunicación que varían en el tiempo | Neuros | 2024 | [PUB] | |
| Lanza: Inversión exacta de gradiente de lotes en el aprendizaje federado | Neuros | 2024 | [PUB] | |
| Aprendizaje federado bajo participación periódica del cliente y datos heterogéneos: un nuevo algoritmo y análisis de eficiencia de comunicación | Neuros | 2024 | [PUB] | |
| Bridging Gaps: agrupación de visión múltiple federada en vistas híbridas heterogéneas | Neuros | 2024 | [PUB] | |
| Aprendizaje federado resistente a la confusión a través de la armonización de datos basada en la difusión en datos que no son IID | Neuros | 2024 | [PUB] | |
| Sopas superiores locales: un catalizador para la fusión modelo en el aprendizaje federado | Neuros | 2024 | [PUB] | |
| Formación de colaboración de libre conductor y conflicto para el aprendizaje federado de Silo Cross-Silo | Neuros | 2024 | [PUB] | |
| Agrupación del clasificador y alineación de características para el aprendizaje federado bajo la deriva del concepto distribuido | Neuros | 2024 | [PUB] | |
| Muestreo de cliente guiado por heterogeneidad: hacia el aprendizaje federado no IID rápido y eficiente | Neuros | 2024 | [PUB] | |
| Hecho o ficción: ¿Pueden los mecanismos veraces eliminar la conducción libre federada? | Neuros | 2024 | [PUB] | |
| Aprendizaje de preferencia activa para ordenar elementos dentro y fuera de la muestra | Neuros | 2024 | [PUB] | |
| Ajuste fino federado de grandes modelos de idiomas bajo tareas heterogéneas y recursos del cliente | Neuros | 2024 | [PUB] | |
| Personalización de ajuste fino en el aprendizaje federado para mitigar a los clientes adversos | Neuros | 2024 | [PUB] | |
| Revisando el conjunto en el aprendizaje federado de un solo disparo | Neuros | 2024 | [PUB] | |
| Fedllm Bench: puntos de referencia realistas para el aprendizaje federado de modelos de idiomas grandes | Neuros | 2024 | [PUB] | |
| $ exttt {PFL-Research} $: Marco de simulación para acelerar la investigación en aprendizaje federado privado | Neuros | 2024 | [PUB] | |
| Fedmeki: un punto de referencia para escalar modelos de Fundación Médica a través de la inyección de conocimiento federado | Neuros | 2024 | [PUB] | |
| Aproximación de impulso en el aprendizaje federado privado asincrónico | Taller Neurips | 2024 | [PUB] | |
| Squeeze de cohorte: más allá de una sola ronda de comunicación por cohorte en el aprendizaje federado de los dispositivos cruzados | Taller Neurips | 2024 | [PUB] | |
| Aprendizaje federado con contenido generativo | Taller Neurips | 2024 | [PUB] | |
| Aprovechando datos de texto no estructurados para el ajuste de instrucciones federadas de modelos de idiomas grandes | Taller Neurips | 2024 | [PUB] | |
| Ataque de seguridad y defensa emergentes en la instrucción federada de modelos de idiomas grandes | Taller Neurips | 2024 | [PUB] | |
| Colaboración sin deserción entre competidores en un sistema de aprendizaje | Taller Neurips | 2024 | [PUB] | |
| Sobre las tasas de convergencia del aprendizaje Q federado en entornos heterogéneos | Taller Neurips | 2024 | [PUB] | |
| Encluster: traer cifrado funcional en modelos fundamentales federados | Taller Neurips | 2024 | [PUB] | |
| Hurto: ajuste de parámetro completo federado a escala para modelos de idiomas grandes | Taller Neurips | 2024 | [PUB] | |
| Aprendizaje federado en caliente | Taller Neurips | 2024 | [PUB] | |
| Entrenamiento dinámico de bajo rango dinámico con garantías de convergencia de pérdida global | Taller Neurips | 2024 | [PUB] | |
| El futuro del modelo de pre-entrenamiento del modelo de lenguaje grande se federe | Taller Neurips | 2024 | [PUB] | |
| Aprendizaje colaborativo con representaciones lineales compartidas: tasas estadísticas y algoritmos óptimos | Taller Neurips | 2024 | [PUB] | |
| El fenómeno de Synapticcity: cuando todos los modelos de fundación se casan con el aprendizaje federado y la cadena de bloques | Taller Neurips | 2024 | [PUB] | |
| Zoopfl: Explorando modelos de base de caja negra para el aprendizaje federado personalizado | Taller Neurips | 2024 | [PUB] | |
| Decomfl: aprendizaje federado con comunicación sin dimensiones | Taller Neurips | 2024 | [PUB] | |
| Mejora de la conectividad grupal para la generalización del aprendizaje profundo federado | Taller Neurips | 2024 | [PUB] | |
| Mapa: Modelo de fusión con el frente de Pareto amortizado utilizando un cálculo limitado | Taller Neurips | 2024 | [PUB] | |
| OPA: agregación privada de una sola vez con interacción de un solo cliente y sus aplicaciones al aprendizaje federado | Taller Neurips | 2024 | [PUB] | |
| Poda del modelo híbrido adaptativo en el aprendizaje federado a través de la exploración de pérdidas | Taller Neurips | 2024 | [PUB] | |
| Capacitación federada en todo el mundo de modelos de idiomas | Taller Neurips | 2024 | [PUB] | |
| Fedstein: Mejora del aprendizaje federado de múltiples dominios a través del estimador de James-Stein | Taller Neurips | 2024 | [PUB] | |
| Mejorar el descubrimiento causal en entornos federados con muestras locales limitadas | Taller Neurips | 2024 | [PUB] | |
| $ exttt {PFL-Research} $: Marco de simulación para acelerar la investigación en aprendizaje federado privado | Taller Neurips | 2024 | [PUB] | |
| DMM: Mecanismo de matriz distribuida para el aprendizaje federado de manera diferencial utilizando el intercambio secreto empacado | Taller Neurips | 2024 | [PUB] | |
| FedCBO: Alcanzar el consenso grupal en el aprendizaje federado en clúster a través de la optimización basada en el consenso | Jmlr | 2024 | [PUB] | |
| Coincidencia de gráficos federado efectivo | ICML | 2024 | [PUB] | |
| Comprensión del aprendizaje federado asistido por servidor en presencia de una participación de cliente incompleta | ICML | 2024 | [PUB] | |
| Beyond the Federation: aprendizaje federado por topología para la generalización a clientes invisibles | ICML | 2024 | [PUB] | |
| FedBPT: ajuste de solicitud de caja negra federada eficiente para modelos de idiomas grandes | ICML | 2024 | [PUB] | |
| Heterogeneidad del modelo de puente en el aprendizaje federado a través de la incertidumbre basada en el aprendizaje por reciprocidad | ICML | 2024 | [PUB] | |
| Una nueva perspectiva teórica sobre la heterogeneidad de los datos en la optimización federada | ICML | 2024 | [PUB] | |
| Mejorar el almacenamiento y la eficiencia computacional en el aprendizaje multimodal federado para modelos a gran escala | ICML | 2024 | [] | |
| Momento para la victoria: aprendizaje de refuerzo federado colaborativo en entornos heterogéneos | ICML | 2024 | [PUB] | |
| Aprendizaje federado bizantino-robust: impacto del subconjunto de clientes y actualizaciones locales | ICML | 2024 | [PUB] | |
| Beneficios comprobables de los pasos locales en el aprendizaje federado heterogéneo para redes neuronales: una perspectiva de aprendizaje de características | ICML | 2024 | [PUB] | |
| Acelerar el aprendizaje federado con estimación media distribuida rápida | ICML | 2024 | [PUB] | |
| Aprendizaje federado a través del núcleo de veto proporcional | ICML | 2024 | [PUB] | |
| AEGISFL: Aprendizaje federado de Silo-Silo-eficiente y flexible que preservan la privacidad-Robust Federal | ICML | 2024 | [PUB] | |
| Recuperación de etiquetas de actualizaciones locales en el aprendizaje federado | ICML | 2024 | [PUB] | |
| Fedmbridge: aprendizaje federado multimodal enrollable | ICML | 2024 | [PUB] | |
| Armonizar la generalización y la personalización en el aprendizaje rápido federado | ICML | 2024 | [PUB] | |
| Las perturbaciones globales estimadas localmente son mejores que las perturbaciones locales para la minimización de la nitidez federada | ICML | 2024 | [PUB] | |
| Acelerando el aprendizaje federado heterogéneo con clasificadores de forma cerrada | ICML | 2024 | [PUB] | |
| Bandidos combinatorios de múltiples agentes combinados | ICML | 2024 | [PUB] | |
| Un método de descenso de gradiente de composición estocástica doblemente recursiva para la optimización de composición de niveles múltiples federados | ICML | 2024 | [PUB] | |
| Aprendizaje federado heterogéneo privado sin un servidor de confianza revisitado: algoritmos óptimos y eficientes de comunicación para pérdidas convexas | ICML | 2024 | [PUB] | |
| FEDRC: abordar diversos cambios en los cambios de distribución en el aprendizaje federado mediante una agrupación robusta | ICML | 2024 | [PUB] | |
| Perseguir el bienestar general en el aprendizaje federado a través de la toma de decisiones secuenciales | ICML | 2024 | [PUB] | |
| Pre-Texto: Modelos de lenguaje de capacitación en datos federados privados en la era de LLMS | ICML | 2024 | [PUB] | |
| Agregación de entropía autónoma para aprendizaje federado heterogéneo bizantino-robuste | ICML | 2024 | [PUB] | |
| Superar los datos y las heterogeneidades del modelo en el aprendizaje federado descentralizado a través de anclajes sintéticos | ICML | 2024 | [PUB] | |
| Optimización federada con corrección de deriva doblemente regularizada | ICML | 2024 | [PUB] | |
| FEDSC: aprendizaje auto-supervisado federado comprobado con objetivo de contraste espectral sobre datos que no son IID | ICML | 2024 | [PUB] | |
| Certificadamente predicción conforme federada bizantina-robust | ICML | 2024 | [PUB] | |
| Lograr la dispersión de gradiente sin pérdidas a través del mapeo al espacio alternativo en el aprendizaje federado | ICML | 2024 | [PUB] | |
| Aprendizaje federado agrupado a través de partición basada en gradientes | ICML | 2024 | [PUB] | |
| Salidas tempranas recurrentes para el aprendizaje federado con clientes heterogéneos | ICML | 2024 | [PUB] | |
| Repensar la búsqueda de mínimos planos en el aprendizaje federado | ICML | 2024 | [PUB] | |
| FedBat: aprendizaje federado eficiente en comunicación a través de binarización aprendida | ICML | 2024 | [PUB] | |
| Aprendizaje de representación federada en el régimen submarimetrario | ICML | 2024 | [PUB] | |
| Fedlmt: Heterogeneidad del sistema de abordaje del aprendizaje federado a través de capacitación de modelos de bajo rango con garantías teóricas | ICML | 2024 | [PUB] | |
| Algoritmo consciente del ruido para el aprendizaje federado heterogéneo diferencialmente privado | ICML | 2024 | [PUB] | |
| Plata: reducción de varianza de un solo circuito y aplicación al aprendizaje federado | ICML | 2024 | [PUB] | |
| Signsgd con defensa federada: aprovechar los ataques adversos a través de la decodificación de letreros de gradiente | ICML | 2024 | [PUB] | |
| FedCal: Lograr la calibración local y global en el aprendizaje federado a través de escalador parametrizado agregado | ICML | 2024 | [PUB] | |
| Aprendizaje continuo federado a través de una transferencia de conocimiento dual basada en indicación | ICML | 2024 | [PUB] | |
| Ajuste de parámetro completo federado de modelos de idiomas de tamaño mil millones con costos de comunicación de menos de 18 kilobytes | ICML | 2024 | [PUB] | |
| Maximización submodular descomponible en el entorno federado | ICML | 2024 | [PUB] | |
| Optimización convexa estocástica privada y federada: estrategias eficientes para sistemas centralizados | ICML | 2024 | [PUB] | |
| Modelado mejorado de conjuntos de datos federados utilizando mezclas de Dirichlet-Multinomios | ICML | 2024 | [PUB] | |
| Lecciones del análisis de errores de generalización del aprendizaje federado: ¡puede comunicarse con menos frecuencia! | ICML | 2024 | [PUB] | |
| Aprendizaje bizantino resistente y federado rápido y federado | ICML | 2024 | [PUB] | |
| Aprendizaje invariante federado personalizado motivado con motivación causalmente con regularización teórica de información directa | ICML | 2024 | [PUB] | |
| Selección de imitación de cliente basada en la clasificación para aprendizaje federado eficiente | ICML | 2024 | [PUB] | |
| Hacia la teoría del aprendizaje federado no supervisado: análisis no asintótico de algoritmos EM federados | ICML | 2024 | [PUB] | |
| Fadas: hacia la optimización asincrónica adaptativa federada | ICML | 2024 | [PUB] | |
| Aprendizaje de refuerzo fuera de línea federado: la cobertura colaborativa de una sola política es suficiente | ICML | 2024 | [PUB] | |
| FedReDefense: defensa contra ataques de envenenamiento modelo para el aprendizaje federado utilizando el error de reconstrucción de la actualización del modelo | ICML | 2024 | [PUB] | |
| MH-PFLID: aprendizaje federado personalizado heterogéneo modelo mediante inyección y destilación para el análisis de datos médicos | ICML | 2024 | [PUB] | |
| Aprendizaje neuro-simbólico federado | ICML | 2024 | [PUB] | |
| Personalización grupal adaptativa para el aprendizaje de transferencia mutua federada | ICML | 2024 | [PUB] | |
| Equilibrando la similitud y la complementariedad para el aprendizaje federado | ICML | 2024 | [PUB] | |
| GNN de autoexplicantes federados con aumentos anti-cortes | ICML | 2024 | [PUB] | |
| Un algoritmo mínimo de composición múltiple estocástica federada para la maximización de AUC profunda | ICML | 2024 | [PUB] | |
| Coala: una plataforma de aprendizaje federada práctica y centrada en la visión | ICML | 2024 | [PUB] | |
| Aprendizaje federado vertical asíncrono seguro y rápido a través de la optimización híbrida en cascada | Mach Learn | 2024 | [PUB] | |
| Aprendizaje federado agrupado de la comunicación a través de la distancia del modelo | USTC; Laboratorio clave del estado de inteligencia cognitiva | Mach Learn | 2024 | [PUB] |
| Aprendizaje federado con agregación supercantil para datos heterogéneos. | Investigación de Google | Mach Learn | 2024 | [Pub] [PDF] [Código] |
| Alineando salidas de modelo para el aprendizaje federado no IID desequilibrado en clase | Nju | Mach Learn | 2024 | [PUB] |
| Aprendizaje federado de redes causales lineales generalizadas | Tpami | 2024 | [PUB] | |
| Reconocimiento de actividad humana federada intermodal | Tpami | 2024 | [PUB] | |
| Proceso gaussiano federado: convergencia, personalización automática y modelado multifidelidad | Universidad del Noreste; Usom | Tpami | 2024 | [Pub] [PDF] [Código] |
| El impacto de los ataques adversos en el aprendizaje federado: una encuesta | IIT | Tpami | 2024 | [PUB] |
| Comprender y mitigar el colapso dimensional en el aprendizaje federado | NUES | Tpami | 2024 | [Pub] [PDF] [Código] |
| Nadie se quedó atrás: aprendizaje de la clase federada del mundo real | Cas; UCAS | Tpami | 2024 | [Pub] [PDF] [Código] |
| Aprendizaje de correlación cruzada e instancia heterogénea heterogénea generalizable y aprendizaje de similitud de instancia | ¿QUÉ? | Tpami | 2024 | [Pub] [PDF] [Código] |
| Aprendizaje federado asincrónico de múltiples etapas con privacidad diferencial adaptativa | HPU; Xjtu | Tpami | 2024 | [Pub] [PDF] [Código] |
| Un marco de aprendizaje federado bayesiano con aproximación en línea de Laplace | Sostener | Tpami | 2024 | [Pub] [PDF] [Código] |
| Mejorar el aprendizaje federado de una sola vez a través de datos y conjunto de conjuntos | USTC; Hkbu | ICLR | 2024 | [PUB] |
| Estimación de privacidad empírica de un solo disparo para el aprendizaje federado | ICLR | 2024 | [Pub] [PDF] | |
| Promedio controlado estocástico para el aprendizaje federado con compresión de comunicación | LinkedIn; Upenn | ICLR | 2024 | [Pub] [PDF] |
| Un método liviano para abordar estadísticas de participación desconocidas en promedio federado | IBM | ICLR | 2024 | [Pub] [PDF] [Código] |
| Una perspectiva de información mutua sobre el aprendizaje contrastante federado | Qualcomm | ICLR | 2024 | [PUB] |
| Algoritmos de evaluación comparativa para la generalización del dominio federado | Universidad de Purdue | ICLR | 2024 | [Pub] [PDF] [Código] |
| Aprendizaje de árbol federado efectivo y eficiente en datos híbridos | Universidad de Berkeley | ICLR | 2024 | [Pub] [PDF] |
| Recomendación federada con personalización aditiva | UTS | ICLR | 2024 | [Pub] [PDF] [Código] |
| Abordar la heterogeneidad de los datos en el aprendizaje federado asincrónico con calibración de actualización en caché | fuente de alimentación | ICLR | 2024 | [Pub] [Supp] |
| Capacitación ortogonal federada: mitigar el olvido catastrófico global en el aprendizaje federado continuo | USC | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Olvidamiento preciso para el aprendizaje continuo federado heterogéneo | JUE | ICLR | 2024 | [Pub] [código] |
| Descubrimiento causal federado a partir de datos heterogéneos | Mbzuai | ICLR | 2024 | [Pub] [PDF] [Código] |
| En bandidos contextuales lineales federados diferencialmente privados | Universidad Estatal de Wayne | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Comunicación veraz incentivada para bandidos federados | Universidad de Virginia | ICLR | 2024 | [Pub] [PDF] |
| Adaptación del dominio federado de principios: proyección de gradiente y peso automático | UIUC | ICLR | 2024 | [PUB] |
| FedP3: poda de red personalizada y amigable para la privacidad bajo heterogeneidad del modelo | Kausto | ICLR | 2024 | [PUB] |
| Generación rápida impulsada por el texto para modelos en idioma de visión en el aprendizaje federado | Robert Bosch LLC | ICLR | 2024 | [Pub] [PDF] |
| Mejora de Lora en el aprendizaje federado de preservación de la privacidad | Universidad del Noreste | ICLR | 2024 | [PUB] |
| Fedwon: Triunfo de aprendizaje federado de múltiples dominios sin normalización | Sony Ai | ICLR | 2024 | [Pub] [PDF] |
| FedTrans: Estimación de servicios públicos transparentes al cliente para un aprendizaje federado robusto | Tu Delft | ICLR | 2024 | [PUB] |
| FedComPass: aprendizaje eficiente de Silo Federado en dispositivos de clientes heterogéneos utilizando un programador informático consciente de alimentación | Anl; Uiuc; NCSA | ICLR | 2024 | [Pub] [PDF] [Código] [página] |
| Optimización de Coreset bayesiano para el aprendizaje federado personalizado | IIT Bombay | ICLR | 2024 | [PUB] |
| Conectividad de modo lineal en modo lineal | Ruhr-Universtät Bochum | ICLR | 2024 | [Pub] [PDF] [Supp] |
| Fingirlo hasta que lo haga: aprendizaje federado con generación orientada a consenso | Sjtu | ICLR | 2024 | [Pub] [PDF] |
| Escondido a la vista: disfrazando los ataques de robo de datos en el aprendizaje federado | Interrogador | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Análisis de tiempo finito del aprendizaje de refuerzo federado heterogéneo en política | Universidad de Columbia | ICLR | 2024 | [Pub] [PDF] |
| Aprendizaje federado adaptativo con clientes automáticos | Universidad de arroz | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Aprendizaje federado de puerta trasera envenenando capas de puerta trasera | DAKOTA DEL NORTE | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Learning Q federado: aceleración lineal de arrepentimiento con bajo costo de comunicación | fuente de alimentación | ICLR | 2024 | [Pub] [Supp] [PDF] |
| FedImpro: medir y mejorar la actualización del cliente en el aprendizaje federado | Hkbu | ICLR | 2024 | [Pub] [PDF] |
| Federada Wasserstein Distancia | MIT | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Un análisis mejorado del recorte por muestra y por actualización en el aprendizaje federado | DTU | ICLR | 2024 | [PUB] |
| FedCDA: aprendizaje federado con agregación consciente de la divergencia cruzada | UNT | ICLR | 2024 | [Pub] [Supp] |
| Gradientes internos de capa cruzada para extender la homogeneidad a la heterogeneidad en el aprendizaje federado | HKU | ICLR | 2024 | [Pub] [PDF] |
| Beneficios de impulso El aprendizaje no federado no IID de manera simple y probable | Pku; Upenn | ICLR | 2024 | [Pub] [PDF] |
| Optimización de bandidos no lineal federada de la comunicación eficiente | Universidad de Yale | ICLR | 2024 | [Pub] [PDF] |
| Valoración de contribución justa y eficiente para el aprendizaje federado vertical | Huawei | ICLR | 2024 | [Pub] [Supp] [PDF] [Código] |
| Desmitificando las compensaciones de equidad local y global en el aprendizaje federado utilizando la descomposición de información parcial | UMCP | ICLR | 2024 | [Pub] [PDF] |
| Aprender representaciones personalizadas causalmente invariantes para clientes federados heterogéneos | Poliu | ICLR | 2024 | [PUB] |
| PEFLL: Aprendizaje federado personalizado aprendiendo a aprender | IST | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Métodos de acento de descenso de gradiente de comunicación para la comunicación para desigualdades variacionales distribuidas: análisis unificado y actualizaciones locales | Jhu | ICLR | 2024 | [Pub] [Supp] [PDF] |
| FedInverse: Evaluación de la fuga de privacidad en el aprendizaje federado | Usq | ICLR | 2024 | [Pub] [Supp] |
| FedDA: métodos de gradiente adaptativos más rápidos para la optimización restringida federada | UMCP | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Capacitación robusta de modelos federados con extremadamente deficiencia de etiqueta | Hkbu | ICLR | 2024 | [Pub] [PDF] [Código] |
| Comprender la convergencia y la generalización en el aprendizaje federado a través de la teoría del aprendizaje de características | Riken AIP | ICLR | 2024 | [PUB] |
| Enseñar LLM a Phish: robar información privada de modelos de idiomas | Universidad de Princeton | ICLR | 2024 | [PUB] |
| Al igual que el aceite y el agua: los métodos de robustez grupales y las defensas de envenenamiento no se mezclan | UMCP | ICLR | 2024 | [PUB] |
| Convergencia acelerada del método estocástico de bola pesada bajo ruido de gradiente anisotrópico | Hkust | ICLR | 2024 | [Pub] [PDF] |
| Hacia la eliminación de las limitaciones de etiquetas duras en los ataques de inversión de gradiente | CAS | ICLR | 2024 | [Pub] [Supp] [PDF] [Código] |
| Optimización de puntos de silla compuesto local | Universidad de Purdue | ICLR | 2024 | [Pub] [PDF] |
| Mejora del entrenamiento neuronal a través de un modelo de dinámica correlacionada | Tiit | ICLR | 2024 | [Pub] [PDF] |
| ECONTROL: optimización distribuida rápida con compresión y control de errores | Universidad del Sarre | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Construcción de ejemplos adversos para el aprendizaje federado vertical: corrupción óptima del cliente a través de bandidos múltiples con múltiples brazos | Hkust | ICLR | 2024 | [PUB] |
| Fundhyper: un programador de tarifas de aprendizaje universal y robusto para el aprendizaje federado con ascendencia hipergradient | UMCP | ICLR | 2024 | [Pub] [Supp] [PDF] [Código] |
| Aprendizaje federado personalizado heterogéneo por actualizaciones globales locales que se mezcla a través de la tasa de convergencia | Cuhk | ICLR | 2024 | [PUB] |
| Rompiendo bordes físicos y lingüísticos: ajuste de inmediato federado multilingüe para idiomas de baja recursos | Universidad de Cambridge | ICLR | 2024 | [PUB] |
| Algoritmo robusto óptimo simple de Minimax para objetivos no convexos con heterogeneidad uniforme de gradiente | NTT Data Mathematical Systems Inc. | ICLR | 2024 | [PUB] |
| VFLAIR: una biblioteca de investigación y un punto de referencia para el aprendizaje federado vertical | JUE | ICLR | 2024 | [Pub] [PDF] [Código] |
| Aprendizaje federado con incentivos con recompensas del modelo de tiempo de entrenamiento | NUES | ICLR | 2024 | [Pub] [Supp] |
| Vertibench: diversidad de distribución de características de avance en puntos de referencia de aprendizaje federado vertical | NUES | ICLR | 2024 | [Pub] [PDF] [Código] |
| FedLoge: aprendizaje federado local y genérico conjunta bajo datos de cola larga | Zju | ICLR | 2024 | [Pub] [Supp] [PDF] |
| SIMFBO: Hacia el aprendizaje bilevel federado simple, flexible y eficiente en la comunicación. | Universidad en Buffalo | Neuros | 2023 | [Pub] [PDF] [Supp] |
| Diseño del mecanismo para la estimación media normal | UW-Madison | Neuros | 2023 | [Pub] [PDF] |
| Aprendizaje distribuido robusto: límites de error apretados y punto de desglose bajo la heterogeneidad de los datos | EPFL | Neuros | 2023 | [Pub] [PDF] [Código] |
| Incentivos en el aprendizaje federado: equilibrios, dinámicas y mecanismos para la maximización del bienestar | UIUC | Neuros | 2023 | [Pub] [Supp] |
| Análisis de convergencia del aprendizaje federado secuencial en datos heterogéneos | Bucear | NeurIPS | 2023 | [PUB] [PDF] [CODE] |
| Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition | MBZUAI | NeurIPS | 2023 | [PUB] [PDF] [CODE] |
| Private Federated Frequency Estimation: Adapting to the Hardness of the Instance | JHU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization | Rutgers University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Incentivized Communication for Federated Bandits | Universidad de Virginia | NeurIPS | 2023 | [PUB] [PDF] |
| Multiply Robust Federated Estimation of Targeted Average Treatment Effects | Universidad del Noreste | NeurIPS | 2023 | [PUB] [PDF] |
| IBA: Towards Irreversible Backdoor Attacks in Federated Learning | Vanderbilt University; VinUniversity | NeurIPS | 2023 | [PUB] [SUPP] [CODE] |
| EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning | KAIST | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Federated Linear Bandits with Finite Adversarial Actions | Universidad de Virginia | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| FedNAR: Federated Optimization with Normalized Annealing Regularization | MBZUAI | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Guiding The Last Layer in Federated Learning with Pre-Trained Models | Concordia University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization | HZAU | NeurIPS | 2023 | [PUB] [SUPP] |
| Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Approach for Object Detection | KAIST | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks | USC | NeurIPS | 2023 | [PUB] [PDF] [CODE] |
| Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning | UTS | NeurIPS | 2023 | [PUB] [SUPP] |
| One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning | Rice University | NeurIPS | 2023 | [PUB] [SUPP] [CODE] |
| Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training | Gatech | NeurIPS | 2023 | [PUB] [SUPP] [CODE] |
| FedGame: A Game-Theoretic Defense against Backdoor Attacks in Federated Learning | PSU; UIUC | NeurIPS | 2023 | [PUB] [SUPP] [CODE] |
| Towards Personalized Federated Learning via Heterogeneous Model Reassembly | fuente de alimentación | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction | GWU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning | ECNU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| A Unified Solution for Privacy and Communication Efficiency in Vertical Federated Learning | universidad occidental | NeurIPS | 2023 | [PUB] [SUPP] [CODE] |
| RECESS Vaccine for Federated Learning: Proactive Defense Against Model Poisoning Attacks | Xidian University; University of Guelph; Zhejiang Key Laboratory of Multi-dimensional Perception Technology, Application and Cybersecurity | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Federated Learning with Bilateral Curation for Partially Class-Disjoint Data | SJTU; Shanghai AI Laboratory | NeurIPS | 2023 | [PUB] [SUPP] [CODE] |
| Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds | GMU; SJTU | NeurIPS | 2023 | [PUB] [SUPP] [CODE] |
| FedL2P: Federated Learning to Personalize | University of Cambridge; Samsung AI Center | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Adaptive Test-Time Personalization for Federated Learning | UIUC | NeurIPS | 2023 | [PUB] [PDF] [CODE] |
| Federated Conditional Stochastic Optimization | universidad de pittsburgh | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Federated Spectral Clustering via Secure Similarity Reconstruction | CUHK | NeurIPS | 2023 | [PUB] |
| Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM | UM-Dearborn | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks | CMU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Federated Multi-Objective Learning | RIT | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout | University of British Columbia; Gatech | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Resolving the Tug-of-War: A Separation of Communication and Learning in Federated Learning | universidad de pittsburgh | NeurIPS | 2023 | [PUB] [SUPP] |
| Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems | universidad de pittsburgh | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| StableFDG: Style and Attention Based Learning for Federated Domain Generalization | KAIST; Purdue University | NeurIPS | 2023 | [PUB] [PDF] |
| Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization | La Universidad de Sídney | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| DELTA: Diverse Client Sampling for Fasting Federated Learning | CUHK; The Shenzhen Institute of Artificial Intelligence and Robotics for Society | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Federated Compositional Deep AUC Maximization | Temple University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| A3FL: Adversarially Adaptive Backdoor Attacks to Federated Learning | fuente de alimentación | NeurIPS | 2023 | [PUB] [SUPP] [CODE] |
| Flow: Per-instance Personalized Federated Learning | University of Massachusetts | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Eliminating Domain Bias for Federated Learning in Representation Space | SJTU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Federated Learning with Manifold Regularization and Normalized Update Reaggregation | POCO | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Structured Federated Learning through Clustered Additive Modeling | University of Technology Sydney | NeurIPS | 2023 | [PUB] [SUPP] |
| Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer | ZJU; Singapore University of Technology and Design | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Dynamic Personalized Federated Learning with Adaptive Differential Privacy | ¿QUÉ? | NeurIPS | 2023 | [PUB] [SUPP] [CODE] |
| Fed-CO$_{2}$ : Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning | ShanghaiTech University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Solving a Class of Non-Convex Minimax Optimization in Federated Learning | universidad de pittsburgh | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Federated Learning via Meta-Variational Dropout | SNU | NeurIPS | 2023 | [PUB] [CODE] |
| Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates | Purdue University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| SPACE: Single-round Participant Amalgamation for Contribution Evaluation in Federated Learning | UNT | NeurIPS | 2023 | [PUB] [CODE] |
| Fed-FA: Theoretically Modeling Client Data Divergence for Federated Language Backdoor Defense | PKU; Tencent | NeurIPS | 2023 | [PUB] [SUPP] |
| FedFed: Feature Distillation against Data Heterogeneity in Federated Learning | BUAA; HKBU | NeurIPS | 2023 | [PUB] [PDF] [CODE] |
| PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning | UCE | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] [解读] |
| Spectral Co-Distillation for Personalized Federated Learning | SUTD | NeurIPS | 2023 | [PUB] |
| Breaking the Communication-Privacy-Accuracy Tradeoff with | ZJU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation | Universidad Stanford | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| (Amplified) Banded Matrix Factorization: A unified approach to private training | Google DeepMind | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices | EQUIPO | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation | Universidad Stanford | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Incentivizing Honesty among Competitors in Collaborative Learning and Optimization | ETH Zúrich | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Resilient Constrained Learning | UPenn | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting | KAUST | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Collaboratively Learning Linear Models with Structured Missing Data | Universidad Stanford | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy | EPFL | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Fast Optimal Locally Private Mean Estimation via Random Projections | Apple Inc. | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Contextual Stochastic Bilevel Optimization | EPFL; ETH Zürich | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Understanding Deep Gradient Leakage via Inversion Influence Functions | MSU; Michigan State University; Universidad de Texas en Austin | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Inner Product-based Neural Network Similarity | Purdue University | NeurIPS | 2023 | [PUB] [SUPP] |
| Correlation Aware Sparsified Mean Estimation Using Random Projection | CMU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| TIES-Merging: Resolving Interference When Merging Models | UNC | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data | Purdue University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Large-Scale Distributed Learning via Private On-Device LSH | UMD | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Faster Relative Entropy Coding with Greedy Rejection Coding | Universidad de Cambridge | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Global Convergence Analysis of Local SGD for Two-layer Neural Network without Overparameterization | SJTU | NeurIPS | 2023 | [PUB] [SUPP] |
| Momentum Provably Improves Error Feedback! | ETH AI Center; ETH Zúrich | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Strategic Data Sharing between Competitors | Sofia University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| H-nobs: Achieving Certified Fairness and Robustness in Distributed Learning on Heterogeneous Datasets | GMU | NeurIPS | 2023 | [PUB] |
| Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking | Wyze Labs | NeurIPS Datasets and Benchmarks | 2023 | [PUB] [SUPP] [DATASET] |
| Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning | Google Research | NeurIPS Datasets and Benchmarks | 2023 | [PUB] [PDF] [DATASET] |
| Text-driven Prompt Generation for Vision-Language Models in Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| HePCo: Data-Free Heterogeneous Prompt Consolidation for Continual Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data | NeurIPS workshop | 2023 | [PUB] | |
| FedSoL: Bridging Global Alignment and Local Generality in Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| One-shot Empirical Privacy Estimation for Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning | NeurIPS workshop | 2023 | [PUB] | |
| SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models | NeurIPS workshop | 2023 | [PUB] | |
| The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| Towards Building the FederatedGPT: Federated Instruction Tuning | NeurIPS workshop | 2023 | [PUB] | |
| Federated Learning for Speech Recognition: Revisiting Current Trends Towards Large-Scale ASR | NeurIPS workshop | 2023 | [PUB] | |
| LASER: Linear Compression in Wireless Distributed Optimization | NeurIPS workshop | 2023 | [PUB] | |
| MARINA Meets Matrix Stepsizes: Variance Reduced Distributed Non-Convex Optimization | NeurIPS workshop | 2023 | [PUB] | |
| TAMUNA: Doubly Accelerated Federated Learning with Local Training, Compression, and Partial Participation | NeurIPS workshop | 2023 | [PUB] | |
| An Empirical Evaluation of Federated Contextual Bandit Algorithms | NeurIPS workshop | 2023 | [PUB] | |
| RealFM: A Realistic Mechanism to Incentivize Data Contribution and Device Participation | NeurIPS workshop | 2023 | [PUB] | |
| FDAPT: Federated Domain-adaptive Pre-training for Language Models | NeurIPS workshop | 2023 | [PUB] | |
| Making Batch Normalization Great in Federated Deep Learning | NeurIPS workshop | 2023 | [PUB] | |
| Correlated Noise Provably Beats Independent Noise for Differentially Private Learning | NeurIPS workshop | 2023 | [PUB] | |
| Parameter Averaging Laws for Multitask Language Models | NeurIPS workshop | 2023 | [PUB] | |
| Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages | NeurIPS workshop | 2023 | [PUB] | |
| Beyond Parameter Averaging in Model Aggregation | NeurIPS workshop | 2023 | [PUB] | |
| Augmenting Federated Learning with Pretrained Transformers | NeurIPS workshop | 2023 | [PUB] | |
| Consensus Optimization at Representation: Improving Personalized Federated Learning via Data-Centric Regularization | NeurIPS workshop | 2023 | [PUB] | |
| DPZero: Dimension-Independent and Differentially Private Zeroth-Order Optimization | NeurIPS workshop | 2023 | [PUB] | |
| Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System | NeurIPS workshop | 2023 | [PUB] | |
| Learning Optimizers for Local SGD | NeurIPS workshop | 2023 | [PUB] | |
| Exploring User-level Gradient Inversion with a Diffusion Prior | NeurIPS workshop | 2023 | [PUB] | |
| User Inference Attacks on Large Language Models | NeurIPS workshop | 2023 | [PUB] | |
| FedLDA: Personalized Federated Learning Through Collaborative Linear Discriminant Analysis | NeurIPS workshop | 2023 | [PUB] | |
| Heterogeneous LoRA for Federated Fine-tuning of On-device Foundation Models | NeurIPS workshop | 2023 | [PUB] | |
| Backdoor Threats from Compromised Foundation Models to Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| MOFL/D: A Federated Multi-objective Learning Framework with Decomposition | NeurIPS workshop | 2023 | [PUB] | |
| Absolute Variation Distance: an Inversion Attack Evaluation Metric for Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| Fed3R: Recursive Ridge Regression for Federated Learning with strong pre-trained models | NeurIPS workshop | 2023 | [PUB] | |
| FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| Private and Personalized Histogram Estimation in a Federated Setting | NeurIPS workshop | 2023 | [PUB] | |
| The Aggregation–Heterogeneity Trade-off in Federated Learning | PKU | POTRO | 2023 | [PUB] |
| FLASH: Automating federated learning using CASH | Instituto Politécnico Rensselaer | UAI | 2023 | [PUB] [SUPP] [MATERIAL] |
| Personalized federated domain adaptation for item-to-item recommendation | AWS AI Labs | UAI | 2023 | [PUB] [PDF] [SUPP] [MATERIAL] [CODE] |
| Fed-LAMB: Layer-wise and Dimension-wise Locally Adaptive Federated Learning | Baidu Research | UAI | 2023 | [PUB] [PDF] [SUPP] [MATERIAL] |
| Federated learning of models pre-trained on different features with consensus graphs | IBM Research | UAI | 2023 | [PUB] [SUPP] [MATERIAL] [CODE] |
| Fast Heterogeneous Federated Learning with Hybrid Client Selection | NWPU | UAI | 2023 | [PUB] [SUPP] [MATERIAL] [PDF] |
| Learning To Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning | Universidad de Cornell | UAI | 2023 | [PUB] [PDF] [SUPP] [MATERIAL] [CODE] |
| Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape | La Universidad de Sídney | ICML | 2023 | [PUB] [PDF] [SLIDES] |
| Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression: Fast Convergence and Partial Participation | Anuncios de LinkedIn | ICML | 2023 | [PUB] [PDF] |
| FedHPO-Bench: A Benchmark Suite for Federated Hyperparameter Optimization | Grupo Alibaba | ICML | 2023 | [PUB] [PDF] [CODE] |
| Federated Conformal Predictors for Distributed Uncertainty Quantification | MIT | ICML | 2023 | [PUB] [PDF] [CODE] |
| Federated Adversarial Learning: A Framework with Convergence Analysis | UBC | ICML | 2023 | [PUB] [PDF] |
| Federated Heavy Hitter Recovery under Linear Sketching | Google Research | ICML | 2023 | [PUB] [PDF] [CODE] |
| Doubly Adversarial Federated Bandits | London School of Economics and Political Science | ICML | 2023 | [PUB] [PDF] [CODE] |
| Achieving Linear Speedup in Non-IID Federated Bilevel Learning | UC | ICML | 2023 | [PUB] [PDF] |
| One-Shot Federated Conformal Prediction | Université Paris-Saclay | ICML | 2023 | [PUB] [PDF] [CODE] |
| Federated Online and Bandit Convex Optimization | TTIC | ICML | 2023 | [PUB] |
| Federated Linear Contextual Bandits with User-level Differential Privacy | The Pennsylvania State University | ICML | 2023 | [PUB] [PDF] |
| Vertical Federated Graph Neural Network for Recommender System | NUES | ICML | 2023 | [PUB] [PDF] [CODE] |
| Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation | University at Buffalo | ICML | 2023 | [PUB] [PDF] |
| Towards Understanding Ensemble Distillation in Federated Learning | KAIST | ICML | 2023 | [PUB] |
| Personalized Subgraph Federated Learning | KAIST | ICML | 2023 | [PUB] [PDF] [CODE] |
| Conformal Prediction for Federated Uncertainty Quantification Under Label Shift | Lagrange Mathematics and Computing Research Center; CMAP | ICML | 2023 | [PUB] [PDF] |
| Secure Federated Correlation Test and Entropy Estimation | CMU | ICML | 2023 | [PUB] [PDF] |
| Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships | JLU | ICML | 2023 | [PUB] [CODE] |
| Personalized Federated Learning under Mixture of Distributions | UCLA | ICML | 2023 | [PUB] [PDF] [CODE] |
| FedDisco: Federated Learning with Discrepancy-Aware Collaboration | SJTU | ICML | 2023 | [PUB] [PDF] [CODE] |
| Anchor Sampling for Federated Learning with Partial Client Participation | Purdue University | ICML | 2023 | [PUB] [PDF] [CODE] |
| Private Federated Learning with Autotuned Compression | JHU; Google | ICML | 2023 | [PUB] [PDF] |
| Fast Federated Machine Unlearning with Nonlinear Functional Theory | Universidad de Castaño | ICML | 2023 | [PUB] |
| On the Convergence of Federated Averaging with Cyclic Client Participation | CMU | ICML | 2023 | [PUB] [PDF] |
| Revisiting Weighted Aggregation in Federated Learning with Neural Networks | ZJU | ICML | 2023 | [PUB] [PDF] [CODE] |
| The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond | CMU | ICML | 2023 | [PUB] [PDF] [SLIDES] |
| GuardHFL: Privacy Guardian for Heterogeneous Federated Learning | UESTC; UNT | ICML | 2023 | [PUB] |
| Flash: Concept Drift Adaptation in Federated Learning | University of Massachusetts | ICML | 2023 | [PUB] |
| DoCoFL: Downlink Compression for Cross-Device Federated Learning | VMware Research; Technion | ICML | 2023 | [PUB] [PDF] |
| FeDXL: Provable Federated Learning for Deep X-Risk Optimization | Universidad A&M de Texas | ICML | 2023 | [PUB] [PDF] [CODE] |
| No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation | GOLPEAR | ICML | 2023 | [PUB] [CODE] |
| Personalized Federated Learning with Inferred Collaboration Graphs | SJTU | ICML | 2023 | [PUB] [CODE] |
| Optimizing the Collaboration Structure in Cross-Silo Federated Learning | UIUC | ICML | 2023 | [PUB] [PDF] [CODE] [SLIDES] |
| TabLeak: Tabular Data Leakage in Federated Learning | ETH Zúrich | ICML | 2023 | [PUB] [PDF] [CODE] |
| FedCR: Personalized Federated Learning Based on Across-Client Common Representation with Conditional Mutual Information Regularization | SJTU | ICML | 2023 | [PUB] [CODE] |
| Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction | Universidad de Duke | ICML | 2023 | [PUB] [PDF] |
| Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design | Meta IA | ICML | 2023 | [PUB] [PDF] [CODE] |
| SRATTA: Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning | Owkin Inc. | ICML | 2023 | [PUB] [PDF] [CODE] |
| Improving the Model Consistency of Decentralized Federated Learning | JUE | ICML | 2023 | [PUB] [PDF] |
| Efficient Personalized Federated Learning via Sparse Model-Adaptation | Grupo Alibaba | ICML | 2023 | [PUB] [PDF] [CODE] |
| From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning | Univ. Lille | ICML | 2023 | [PUB] [PDF] [CODE] |
| LeadFL: Client Self-Defense against Model Poisoning in Federated Learning | TUD | ICML | 2023 | [PUB] [CODE] |
| Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning | HKUST | ICML | 2023 | [PUB] [PDF] [CODE] |
| FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models | HKUST | ICML | 2023 | [PUB] [PDF] |
| FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction | CUHK; The Shenzhen Institute of Artificial Intelligence and Robotics for Society | ICML | 2023 | [PUB] [PDF] [CODE] |
| Towards Unbiased Training in Federated Open-world Semi-supervised Learning | PolyU | ICML | 2023 | [PUB] [PDF] [SLIDES] |
| Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis | Georgia Tech; Meta IA | ICML | 2023 | [PUB] [PDF] |
| Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning | KU Lovaina | ICML | 2023 | [PUB] [PDF] [CODE] |
| Fair yet Asymptotically Equal Collaborative Learning | NUES | ICML | 2023 | [PUB] [PDF] [CODE] |
| Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability | Adobe Research | ICML | 2023 | [PUB] [PDF] |
| Adversarial Collaborative Learning on Non-IID Features | UC Berkeley; NUES | ICML | 2023 | [PUB] |
| XTab: Cross-table Pretraining for Tabular Transformers | EPFL; Cornell University; AWS | ICML | 2023 | [PUB] [PDF] [CODE] |
| Momentum Ensures Convergence of SIGNSGD under Weaker Assumptions | NUDT | ICML | 2023 | [PUB] |
| Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting | Key Lab of Intelligent Computing Based Big Data of Zhejiang Province; ZJU; Sony Al | ICML | 2023 | [PUB] [PDF] [CODE] |
| LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning | Instituto Politécnico Rensselaer | ICML | 2023 | [PUB] [PDF] |
| FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks | Universidad de Minnesota | ICML | 2023 | [PUB] |
| Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm | universidad de chicago | ICML | 2023 | [PUB] [PDF] [CODE] |
| Ensemble and continual federated learning for classification tasks. | Universidade de Santiago de Compostela | Mach Learn | 2023 | [PUB] [PDF] |
| FAC-fed: Federated adaptation for fairness and concept drift aware stream classification | Leibniz University of Hannover | Mach Learn | 2023 | [PUB] |
| Robust federated learning under statistical heterogeneity via hessian-weighted aggregation | Deakin University | Mach Learn | 2023 | [PUB] |
| FedLab: A Flexible Federated Learning Framework | UESTC; Peng Cheng Lab | JMLR | 2023 | [PUB] [PDF] [CODE] |
| Minimax Estimation for Personalized Federated Learning: An Alternative between FedAvg and Local Training? | JMLR | 2023 | [PUB] | |
| Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning | TAMU | JMLR | 2023 | [PUB] [PDF] [CODE] |
| A First Look into the Carbon Footprint of Federated Learning | Universidad de Cambridge | JMLR | 2023 | [PUB] [PDF] |
| Attacks against Federated Learning Defense Systems and their Mitigation | The University of Newcastle | JMLR | 2023 | [PUB] [CODE] |
| A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates | Universit ́e Cˆ ote d'Azur | JMLR | 2023 | [PUB] [PDF] [CODE] |
| Tighter Regret Analysis and Optimization of Online Federated Learning | Hanyang University | TPAMI | 2023 | [PUB] [PDF] |
| Efficient Federated Learning Via Local Adaptive Amended Optimizer With Linear Speedup | University of Sydney | TPAMI | 2023 | [PDF] |
| Federated Learning Via Inexact ADMM. | BJTU | TPAMI | 2023 | [PUB] [PDF] [CODE] |
| FedIPR: Ownership Verification for Federated Deep Neural Network Models | SJTU | TPAMI | 2023 | [PUB] [PDF] [CODE] [解读] |
| Decentralized Federated Averaging | NUDT | TPAMI | 2023 | [PUB] [PDF] |
| Personalized Federated Learning with Feature Alignment and Classifier Collaboration | JUE | ICLR | 2023 | [PUB] [CODE] |
| MocoSFL: enabling cross-client collaborative self-supervised learning | ASU | ICLR | 2023 | [PUB] [CODE] |
| Single-shot General Hyper-parameter Optimization for Federated Learning | IBM | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Where to Begin? Exploring the Impact of Pre-Training and Initialization in Federated | ICLR | 2023 | [PUB] [PDF] [CODE] | |
| FedExP: Speeding up Federated Averaging via Extrapolation | CMU | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection | Universidad Estatal de Michigan | ICLR | 2023 | [PUB] [CODE] |
| DASHA: Distributed Nonconvex Optimization with Communication Compression and Optimal Oracle Complexity | KAUST | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Machine Unlearning of Federated Clusters | University of Illinois | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Federated Neural Bandits | NUES | ICLR | 2023 | [PUB] [PDF] [CODE] |
| FedFA: Federated Feature Augmentation | ETH Zúrich | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach | CMU | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Better Generative Replay for Continual Federated Learning | Universidad de Virginia | ICLR | 2023 | [PUB] [CODE] |
| Federated Learning from Small Datasets | IKIM | ICLR | 2023 | [PUB] [PDF] |
| Federated Nearest Neighbor Machine Translation | USTC | ICLR | 2023 | [PUB] [PDF] |
| Meta Knowledge Condensation for Federated Learning | A*STAR | ICLR | 2023 | [PUB] [PDF] |
| Test-Time Robust Personalization for Federated Learning | EPFL | ICLR | 2023 | [PUB] [PDF] [CODE] |
| DepthFL : Depthwise Federated Learning for Heterogeneous Clients | SNU | ICLR | 2023 | [PUB] |
| Towards Addressing Label Skews in One-Shot Federated Learning | NUES | ICLR | 2023 | [PUB] [CODE] |
| Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning | NUES | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation | UMD | ICLR | 2023 | [PUB] [CODE] |
| SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication | UMD | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses | USC | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Effective passive membership inference attacks in federated learning against overparameterized models | Purdue University | ICLR | 2023 | [PUB] |
| FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification | Universidad de Cambridge | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Multimodal Federated Learning via Contrastive Representation Ensemble | JUE | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Faster federated optimization under second-order similarity | Universidad de Princeton | ICLR | 2023 | [PUB] [PDF] [CODE] |
| FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy | University of Sydney | ICLR | 2023 | [PUB] [CODE] |
| The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation | utexas | ICLR | 2023 | [PUB] [PDF] [CODE] |
| PerFedMask: Personalized Federated Learning with Optimized Masking Vectors | UBC | ICLR | 2023 | [PUB] [CODE] |
| EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data | GMU | ICLR | 2023 | [PUB] [CODE] |
| FedDAR: Federated Domain-Aware Representation Learning | harvard | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning | upenn | ICLR | 2023 | [PUB] [CODE] |
| FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning | Purdue University | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses | RUC | ICLR | 2023 | [PUB] |
| Efficient Federated Domain Translation | Purdue University | ICLR | 2023 | [PUB] [CODE] |
| On the Importance and Applicability of Pre-Training for Federated Learning | OSU | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models | UMD | ICLR | 2023 | [PUB] [PDF] [CODE] |
| A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy | UCLA | ICLR | 2023 | [PUB] [PDF] |
| Instance-wise Batch Label Restoration via Gradients in Federated Learning | BUAA | ICLR | 2023 | [PUB] [CODE] |
| Data-Free One-Shot Federated Learning Under Very High Statistical Heterogeneity | College of William and Mary | ICLR | 2023 | [PUB] |
| CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning | Universidad de Warwick | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Sparse Random Networks for Communication-Efficient Federated Learning | stanford | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Combating Exacerbated Heterogeneity for Robust Decentralized Models | HKBU | ICLR | 2023 | [PUB] [CODE] |
| Hyperparameter Optimization through Neural Network Partitioning | Universidad de Cambridge | ICLR | 2023 | [PUB] [PDF] |
| Does Decentralized Learning with Non-IID Unlabeled Data Benefit from Self Supervision? | MIT | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top | mbzuai | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Dual Diffusion Implicit Bridges for Image-to-Image Translation | stanford | ICLR | 2023 | [PUB] [PDF] [CODE] |
| An accurate, scalable and verifiable protocol for federated differentially private averaging | INRIA Lille | Mach Learn | 2022 | [PUB] [PDF] |
| Federated online clustering of bandits. | CUHK | UAI | 2022 | [PUB] [PDF] [CODE] |
| Privacy-aware compression for federated data analysis. | Meta IA | UAI | 2022 | [PUB] [PDF] [CODE] |
| Faster non-convex federated learning via global and local momentum. | UTEXAS | UAI | 2022 | [PUB] [PDF] |
| Fedvarp: Tackling the variance due to partial client participation in federated learning. | CMU | UAI | 2022 | [PUB] [PDF] |
| SASH: Efficient secure aggregation based on SHPRG for federated learning | CAS; CASTEST | UAI | 2022 | [PUB] [PDF] |
| Bayesian federated estimation of causal effects from observational data | NUES | UAI | 2022 | [PUB] [PDF] |
| Communication-Efficient Randomized Algorithm for Multi-Kernel Online Federated Learning | Hanyang University | TPAMI | 2022 | [PUB] |
| Lazily Aggregated Quantized Gradient Innovation for Communication-Efficient Federated Learning | ZJU | TPAMI | 2022 | [PUB] [CODE] |
| Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox | Moscow Institute of Physics and Technology | NeurIPS | 2022 | [PUB] [PDF] |
| LAMP: Extracting Text from Gradients with Language Model Priors | ETHZ | NeurIPS | 2022 | [PUB] [CODE] |
| FedAvg with Fine Tuning: Local Updates Lead to Representation Learning | utexas | NeurIPS | 2022 | [PUB] [PDF] |
| On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond | NUIST | NeurIPS | 2022 | [PUB] [PDF] |
| Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams | WISC | NeurIPS | 2022 | [PUB] [CODE] |
| Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks | Universidad de Columbia | NeurIPS | 2022 | [PUB] [PDF] |
| Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective | PKU | NeurIPS | 2022 | [PUB] |
| Subspace Recovery from Heterogeneous Data with Non-isotropic Noise | stanford | NeurIPS | 2022 | [PUB] [PDF] |
| EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization | KAUST | NeurIPS | 2022 | [PUB] [PDF] |
| On-Demand Sampling: Learning Optimally from Multiple Distributions | Universidad de Berkeley | NeurIPS | 2022 | [PUB] [CODE] |
| Improved Utility Analysis of Private CountSketch | UIT | NeurIPS | 2022 | [PUB] [PDF] [CODE] |
| Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning | HUAWEI | NeurIPS | 2022 | [PUB] [CODE] |
| Decentralized Local Stochastic Extra-Gradient for Variational Inequalities | phystech | NeurIPS | 2022 | [PUB] [PDF] |
| BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compression | Princeton | NeurIPS | 2022 | [PUB] [PDF] [CODE] |
| Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning | The University of Tokyo | NeurIPS | 2022 | [PUB] [PDF] |
| Near-Optimal Collaborative Learning in Bandits | INRIA; Inserm | NeurIPS | 2022 | [PUB] [PDF] [CODE] |
| Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees | phystech | NeurIPS | 2022 | [PUB] [PDF] |
| Towards Optimal Communication Complexity in Distributed Non-Convex Optimization | TTIC | NeurIPS | 2022 | [PUB] [CODE] |
| FedPop: A Bayesian Approach for Personalised Federated Learning | Skoltech | NeurIPS | 2022 | [PUB] [PDF] |
| Fairness in Federated Learning via Core-Stability | UIUC | NeurIPS | 2022 | [PUB] [CODE] |
| SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning | Sorbonne Université | NeurIPS | 2022 | [PUB] [PDF] |
| FedRolex: Model-Heterogeneous Federated Learning with Rolling Submodel Extraction | Universidad Estatal de Michigan | NeurIPS | 2022 | [PUB] [CODE] |
| On Sample Optimality in Personalized Collaborative and Federated Learning | INRIA | NeurIPS | 2022 | [PUB] |
| DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing | HKUST | NeurIPS | 2022 | [PUB] [PDF] |
| FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning | JUE | NeurIPS | 2022 | [PUB] |
| Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning | KAUST | NeurIPS | 2022 | [PUB] [PDF] |
| VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely? | ¿QUÉ? | NeurIPS | 2022 | [PUB] [CODE] |
| DENSE: Data-Free One-Shot Federated Learning | ZJU | NeurIPS | 2022 | [PUB] [PDF] |
| CalFAT: Calibrated Federated Adversarial Training with Label Skewness | ZJU | NeurIPS | 2022 | [PUB] [PDF] |
| SAGDA: Achieving O(ϵ−2) Communication Complexity in Federated Min-Max Learning | OSU | NeurIPS | 2022 | [PUB] [PDF] |
| Taming Fat-Tailed (“Heavier-Tailed” with Potentially Infinite Variance) Noise in Federated Learning | OSU | NeurIPS | 2022 | [PUB] [PDF] |
| Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness | PKU | NeurIPS | 2022 | [PUB] |
| Federated Submodel Optimization for Hot and Cold Data Features | SJTU | NeurIPS | 2022 | [PUB] |
| BooNTK: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels | Universidad de Berkeley | NeurIPS | 2022 | [PUB] [PDF] |
| Byzantine-tolerant federated Gaussian process regression for streaming data | fuente de alimentación | NeurIPS | 2022 | [PUB] [CODE] |
| SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression | CMU | NeurIPS | 2022 | [PUB] [PDF] |
| Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clustering | Yale | NeurIPS | 2022 | [PUB] [PDF] [CODE] |
| Communication Efficient Federated Learning for Generalized Linear Bandits | Universidad de Virginia | NeurIPS | 2022 | [PUB] [CODE] |
| Recovering Private Text in Federated Learning of Language Models | Princeton | NeurIPS | 2022 | [PUB] [PDF] [CODE] |
| Federated Learning from Pre-Trained Models: A Contrastive Learning Approach | UTS | NeurIPS | 2022 | [PUB] [PDF] |
| Global Convergence of Federated Learning for Mixed Regression | Universidad del Noreste | NeurIPS | 2022 | [PUB] [PDF] |
| Resource-Adaptive Federated Learning with All-In-One Neural Composition | JHU | NeurIPS | 2022 | [PUB] |
| Self-Aware Personalized Federated Learning | Amazonas | NeurIPS | 2022 | [PUB] [PDF] |
| A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning | Universidad del Noreste | NeurIPS | 2022 | [PUB] [PDF] |
| An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects | NUES | NeurIPS | 2022 | [PUB] |
| Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning | EPFL | NeurIPS | 2022 | [PUB] [PDF] |
| Personalized Online Federated Multi-Kernel Learning | UCI | NeurIPS | 2022 | [PUB] |
| SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training | Universidad de Duke | NeurIPS | 2022 | [PUB] [PDF] [CODE] |
| A Unified Analysis of Federated Learning with Arbitrary Client Participation | IBM | NeurIPS | 2022 | [PUB] [PDF] |
| Preservation of the Global Knowledge by Not-True Distillation in Federated Learning | KAIST | NeurIPS | 2022 | [PUB] [PDF] [CODE] |
| FedSR: A Simple and Effective Domain Generalization Method for Federated Learning | Universidad de Oxford | NeurIPS | 2022 | [PUB] [CODE] |
| Factorized-FL: Personalized Federated Learning with Parameter Factorization & Similarity Matching | KAIST | NeurIPS | 2022 | [PUB] [PDF] [CODE] |
| A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits | UC | NeurIPS | 2022 | [PUB] [PDF] |
| Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework | Universidad de Tulane | NeurIPS | 2022 | [PUB] |
| On Privacy and Personalization in Cross-Silo Federated Learning | CMU | NeurIPS | 2022 | [PUB] [PDF] |
| A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning | NUES | NeurIPS | 2022 | [PUB] [PDF] [CODE] |
| FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings | Owkin | NeurIPS Datasets and Benchmarks | 2022 | [PUB] [CODE] |
| A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources | universidad de pittsburgh | ICML | 2022 | [PUB] [PDF] [CODE] |
| Fast Composite Optimization and Statistical Recovery in Federated Learning | SJTU | ICML | 2022 | [PUB] [PDF] [CODE] |
| Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning | Universidad de Nueva York | ICML | 2022 | [PUB] [PDF] [CODE] |
| The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning | Stanford; Google Research | ICML | 2022 | [PUB] [PDF] [CODE] [SLIDE] |
| The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation | Stanford; Google Research | ICML | 2022 | [PUB] [PDF] [CODE] |
| DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training | USTC | ICML | 2022 | [PUB] [PDF] [CODE] |
| FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning | University of Oulu | ICML | 2022 | [PUB] [PDF] [CODE] |
| DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning | Universidad de Cambridge | ICML | 2022 | [PUB] [PDF] [SLIDE] [CODE] |
| Accelerated Federated Learning with Decoupled Adaptive Optimization | Universidad de Castaño | ICML | 2022 | [PUB] [PDF] |
| Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling | Tecnología de Georgia | ICML | 2022 | [PUB] [PDF] |
| Multi-Level Branched Regularization for Federated Learning | Universidad Nacional de Seúl | ICML | 2022 | [PUB] [PDF] [CODE] [PAGE] |
| FedScale: Benchmarking Model and System Performance of Federated Learning at Scale | Universidad de Michigan | ICML | 2022 | [PUB] [PDF] [CODE] |
| Federated Learning with Positive and Unlabeled Data | XJTU | ICML | 2022 | [PUB] [PDF] [CODE] |
| Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning | SJTU | ICML | 2022 | [PUB] [CODE] |
| Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering | Universidad de Michigan | ICML | 2022 | [PUB] [PDF] [CODE] |
| Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring | USTC | ICML | 2022 | [PUB] [PDF] [CODE] [SLIDE] [解读] |
| Architecture Agnostic Federated Learning for Neural Networks | La Universidad de Texas en Austin | ICML | 2022 | [PUB] [PDF] [SLIDE] |
| Personalized Federated Learning through Local Memorization | Inria | ICML | 2022 | [PUB] [PDF] [CODE] |
| Proximal and Federated Random Reshuffling | KAUST | ICML | 2022 | [PUB] [PDF] [CODE] |
| Federated Learning with Partial Model Personalization | universidad de washington | ICML | 2022 | [PUB] [PDF] [CODE] |
| Generalized Federated Learning via Sharpness Aware Minimization | University of South Florida | ICML | 2022 | [PUB] [PDF] |
| FedNL: Making Newton-Type Methods Applicable to Federated Learning | KAUST | ICML | 2022 | [PUB] [PDF] [VIDEO] [SLIDE] |
| Federated Minimax Optimization: Improved Convergence Analyses and Algorithms | CMU | ICML | 2022 | [PUB] [PDF] [SLIDE] |
| Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning | Hong Kong Baptist University | ICML | 2022 | [PUB] [PDF] [CODE] [解读] |
| FedNest: Federated Bilevel, Minimax, and Compositional Optimization | Universidad de Michigan | ICML | 2022 | [PUB] [PDF] [CODE] |
| EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning | VMware Research | ICML | 2022 | [PUB] [PDF] [CODE] |
| Communication-Efficient Adaptive Federated Learning | Pennsylvania State University | ICML | 2022 | [PUB] [PDF] |
| ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training | CISPA Helmholz Center for Information Security | ICML | 2022 | [PUB] [PDF] [SLIDE] [CODE] |
| Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification | University of Maryland | ICML | 2022 | [PUB] [PDF] [CODE] |
| Anarchic Federated Learning | La Universidad Estatal de Ohio | ICML | 2022 | [PUB] [PDF] |
| QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning | Nankai University | ICML | 2022 | [PUB] [CODE] |
| Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization | KAIST | ICML | 2022 | [PUB] [PDF] |
| Neural Tangent Kernel Empowered Federated Learning | NC State University | ICML | 2022 | [PUB] [PDF] [CODE] |
| Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy | UMN | ICML | 2022 | [PUB] [PDF] |
| Personalized Federated Learning via Variational Bayesian Inference | CAS | ICML | 2022 | [PUB] [PDF] [SLIDE] [UC.] |
| Federated Learning with Label Distribution Skew via Logits Calibration | ZJU | ICML | 2022 | [PUB] |
| Neurotoxin: Durable Backdoors in Federated Learning | Southeast University;Princeton | ICML | 2022 | [PUB] [PDF] [CODE] |
| Resilient and Communication Efficient Learning for Heterogeneous Federated Systems | Universidad Estatal de Michigan | ICML | 2022 | [PUB] |
| Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond | KAIST | ICLR (oral) | 2022 | [PUB] [CODE] |
| Bayesian Framework for Gradient Leakage | ETH Zúrich | ICLR | 2022 | [PUB] [PDF] [CODE] |
| Federated Learning from only unlabeled data with class-conditional-sharing clients | The University of Tokyo; CUHK | ICLR | 2022 | [PUB] [CODE] |
| FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning | CMU; University of Illinois at Urbana-Champaign; universidad de washington | ICLR | 2022 | [PUB] [PDF] |
| Acceleration of Federated Learning with Alleviated Forgetting in Local Training | JUE | ICLR | 2022 | [PUB] [PDF] [CODE] |
| FedPara: Low-rank Hadamard Product for Communicatkion-Efficient Federated Learning | POSTECH | ICLR | 2022 | [PUB] [PDF] [CODE] |
| An Agnostic Approach to Federated Learning with Class Imbalance | Universidad de Pensilvania | ICLR | 2022 | [PUB] [CODE] |
| Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization | Michigan State University; La Universidad de Texas en Austin | ICLR | 2022 | [PUB] [PDF] [CODE] |
| Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models | University of Maryland; Universidad de Nueva York | ICLR | 2022 | [PUB] [PDF] [CODE] |
| ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity | University of Cambridge; Universidad de Oxford | ICLR | 2022 | [PUB] [PDF] |
| Diverse Client Selection for Federated Learning via Submodular Maximization | Intel; CMU | ICLR | 2022 | [PUB] [CODE] |
| Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank? | Purdue | ICLR | 2022 | [PUB] [PDF] [CODE] |
| Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions | University of Maryland; Google | ICLR | 2022 | [PUB] [CODE] |
| Towards Model Agnostic Federated Learning Using Knowledge Distillation | EPFL | ICLR | 2022 | [PUB] [PDF] [CODE] |
| Divergence-aware Federated Self-Supervised Learning | NTU; SenseTime | ICLR | 2022 | [PUB] [PDF] [CODE] |
| What Do We Mean by Generalization in Federated Learning? | Stanford; Google | ICLR | 2022 | [PUB] [PDF] [CODE] |
| FedBABU: Toward Enhanced Representation for Federated Image Classification | KAIST | ICLR | 2022 | [PUB] [PDF] [CODE] |
| Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing | EPFL | ICLR | 2022 | [PUB] [PDF] [CODE] |
| Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters | Aibee | ICLR Spotlight | 2022 | [PUB] [PDF] [PAGE] [解读] |
| Hybrid Local SGD for Federated Learning with Heterogeneous Communications | University of Texas; Pennsylvania State University | ICLR | 2022 | [PUB] |
| On Bridging Generic and Personalized Federated Learning for Image Classification | La Universidad Estatal de Ohio | ICLR | 2022 | [PUB] [PDF] [CODE] |
| Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond | KAIST; MIT | ICLR | 2022 | [PUB] [PDF] |
| One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them. | JMLR | 2021 | [PUB] [CODE] | |
| Constrained differentially private federated learning for low-bandwidth devices | UAI | 2021 | [PUB] [PDF] | |
| Federated stochastic gradient Langevin dynamics | UAI | 2021 | [PUB] [PDF] | |
| Federated Learning Based on Dynamic Regularization | BU; BRAZO | ICLR | 2021 | [PUB] [PDF] [CODE] |
| Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning | La Universidad Estatal de Ohio | ICLR | 2021 | [PUB] [PDF] |
| HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients | Universidad de Duke | ICLR | 2021 | [PUB] [PDF] [CODE] |
| FedMix: Approximation of Mixup under Mean Augmented Federated Learning | KAIST | ICLR | 2021 | [PUB] [PDF] |
| Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms | CMU; Google | ICLR | 2021 | [PUB] [PDF] [CODE] |
| Adaptive Federated Optimization | ICLR | 2021 | [PUB] [PDF] [CODE] | |
| Personalized Federated Learning with First Order Model Optimization | Stanford; Nvidia | ICLR | 2021 | [PUB] [PDF] [CODE] [UC.] |
| FedBN: Federated Learning on Non-IID Features via Local Batch Normalization | Princeton | ICLR | 2021 | [PUB] [PDF] [CODE] |
| FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning | La Universidad Estatal de Ohio | ICLR | 2021 | [PUB] [PDF] [CODE] |
| Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning | KAIST | ICLR | 2021 | [PUB] [PDF] [CODE] |
| KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation | ZJU | ICML | 2021 | [PUB] [PDF] [CODE] [解读] |
| Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix | Universidad de Harvard | ICML | 2021 | [PUB] [PDF] [VIDEO] [CODE] |
| FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis | PKU; Princeton | ICML | 2021 | [PUB] [PDF] [VIDEO] |
| Personalized Federated Learning using Hypernetworks | Bar-Ilan University; Nvidia | ICML | 2021 | [PUB] [PDF] [CODE] [PAGE] [VIDEO] [解读] |
| Federated Composite Optimization | Stanford; Google | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] [SLIDE] |
| Exploiting Shared Representations for Personalized Federated Learning | University of Texas at Austin; Universidad de Pensilvania | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] |
| Data-Free Knowledge Distillation for Heterogeneous Federated Learning | Universidad Estatal de Michigan | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] |
| Federated Continual Learning with Weighted Inter-client Transfer | KAIST | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] |
| Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity | The University of Iowa | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] |
| Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning | The University of Tokyo | ICML | 2021 | [PUB] [PDF] [VIDEO] |
| Federated Learning of User Verification Models Without Sharing Embeddings | Qualcomm | ICML | 2021 | [PUB] [PDF] [VIDEO] |
| Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning | acento | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] |
| Ditto: Fair and Robust Federated Learning Through Personalization | CMU; Facebook AI | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] |
| Heterogeneity for the Win: One-Shot Federated Clustering | CMU | ICML | 2021 | [PUB] [PDF] [VIDEO] |
| The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] | |
| Debiasing Model Updates for Improving Personalized Federated Training | BU; Brazo | ICML | 2021 | [PUB] [CODE] [VIDEO] |
| One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning | Toyota; Berkeley; Universidad de Cornell | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] |
| CRFL: Certifiably Robust Federated Learning against Backdoor Attacks | UIUC; IBM | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] |
| Federated Learning under Arbitrary Communication Patterns | Indiana University; Amazonas | ICML | 2021 | [PUB] [VIDEO] |
| CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression | CMU | NeurIPS | 2021 | [PUB] [PDF] |
| Boosting with Multiple Sources | NeurIPS | 2021 | [PUB] | |
| DRIVE: One-bit Distributed Mean Estimation | VMware | NeurIPS | 2021 | [PUB] [CODE] |
| Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning | NUES | NeurIPS | 2021 | [PUB] [CODE] |
| Gradient Inversion with Generative Image Prior | POSTECH | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Distributed Machine Learning with Sparse Heterogeneous Data | Universidad de Oxford | NeurIPS | 2021 | [PUB] [PDF] |
| Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning | UCLA | NeurIPS | 2021 | [PUB] [PDF] |
| Sageflow: Robust Federated Learning against Both Stragglers and Adversaries | KAIST | NeurIPS | 2021 | [PUB] |
| CAFE: Catastrophic Data Leakage in Vertical Federated Learning | Rensselaer Polytechnic Institute; IBM Research | NeurIPS | 2021 | [PUB] [CODE] |
| Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee | NUES | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Optimality and Stability in Federated Learning: A Game-theoretic Approach | Universidad de Cornell | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning | UCLA | NeurIPS | 2021 | [PUB] [PDF] [CODE] [解读] |
| The Skellam Mechanism for Differentially Private Federated Learning | Google Research; CMU | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data | NUS; Huawei | NeurIPS | 2021 | [PUB] [PDF] |
| STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning | UMN | NeurIPS | 2021 | [PUB] [PDF] |
| Subgraph Federated Learning with Missing Neighbor Generation | Emory; UBC; Lehigh University | NeurIPS | 2021 | [PUB] [PDF] [CODE] [解读] |
| Evaluating Gradient Inversion Attacks and Defenses in Federated Learning | Princeton | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Personalized Federated Learning With Gaussian Processes | Universidad Bar Ilán | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Differentially Private Federated Bayesian Optimization with Distributed Exploration | MIT; NUES | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Parameterized Knowledge Transfer for Personalized Federated Learning | PolyU | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Federated Reconstruction: Partially Local Federated Learning | Google Research | NeurIPS | 2021 | [PUB] [PDF] [CODE] [UC.] |
| Fast Federated Learning in the Presence of Arbitrary Device Unavailability | JUE; Princeton; MIT | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective | Duke University; Accenture Labs | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout | KAUST; Samsung AI Center | NeurIPS | 2021 | [PUB] [PDF] |
| Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients | Universidad de Pensilvania | NeurIPS | 2021 | [PUB] [PDF] [VIDEO] |
| Federated Multi-Task Learning under a Mixture of Distributions | INRIA; Accenture Labs | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Federated Graph Classification over Non-IID Graphs | emory | NeurIPS | 2021 | [PUB] [PDF] [CODE] [解读] |
| Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing | CMU; Empresa Hewlett Packard | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| On Large-Cohort Training for Federated Learning | Google; CMU | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning | KAUST; Columbia University; University of Central Florida | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization | Huawei | NeurIPS | 2021 | [PUB] [VIDEO] |
| Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis | KAIST | NeurIPS | 2021 | [PUB] [PDF] |
| Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning | JUE; Alibaba; Medicina Weill Cornell | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Federated Linear Contextual Bandits | The Pennsylvania State University; Facebook; Universidad de Virginia | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Few-Round Learning for Federated Learning | KAIST | NeurIPS | 2021 | [PUB] |
| Breaking the centralized barrier for cross-device federated learning | EPFL; Google Research | NeurIPS | 2021 | [PUB] [CODE] [VIDEO] |
| Federated-EM with heterogeneity mitigation and variance reduction | Ecole Polytechnique; Google Research | NeurIPS | 2021 | [PUB] [PDF] |
| Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning | MIT; Amazonas; Google | NeurIPS | 2021 | [PUB] [PAGE] [SLIDE] |
| FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization | University of North Carolina at Chapel Hill; IBM Research | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Federated Adversarial Domain Adaptation | BU; Columbia University; Rutgers University | ICLR | 2020 | [PUB] [PDF] [CODE] |
| DBA: Distributed Backdoor Attacks against Federated Learning | ZJU; IBM Research | ICLR | 2020 | [PUB] [CODE] |
| Fair Resource Allocation in Federated Learning | CMU; Facebook AI | ICLR | 2020 | [PUB] [PDF] [CODE] |
| Federated Learning with Matched Averaging | University of Wisconsin-Madison; IBM Research | ICLR | 2020 | [PUB] [PDF] [CODE] |
| Differentially Private Meta-Learning | CMU | ICLR | 2020 | [PUB] [PDF] |
| Generative Models for Effective ML on Private, Decentralized Datasets | ICLR | 2020 | [PUB] [PDF] [CODE] | |
| On the Convergence of FedAvg on Non-IID Data | PKU | ICLR | 2020 | [PUB] [PDF] [CODE] [解读] |
| FedBoost: A Communication-Efficient Algorithm for Federated Learning | ICML | 2020 | [PUB] [VIDEO] | |
| FetchSGD: Communication-Efficient Federated Learning with Sketching | UC Berkeley; Johns Hopkins University; Amazonas | ICML | 2020 | [PUB] [PDF] [VIDEO] [CODE] |
| SCAFFOLD: Stochastic Controlled Averaging for Federated Learning | EPFL; Google | ICML | 2020 | [PUB] [PDF] [VIDEO] [UC.] [解读] |
| Federated Learning with Only Positive Labels | ICML | 2020 | [PUB] [PDF] [VIDEO] | |
| From Local SGD to Local Fixed-Point Methods for Federated Learning | Moscow Institute of Physics and Technology; KAUST | ICML | 2020 | [PUB] [PDF] [SLIDE] [VIDEO] |
| Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization | KAUST | ICML | 2020 | [PUB] [PDF] [SLIDE] [VIDEO] |
| Differentially-Private Federated Linear Bandits | MIT | NeurIPS | 2020 | [PUB] [PDF] [CODE] |
| Federated Principal Component Analysis | University of Cambridge; Quine Technologies | NeurIPS | 2020 | [PUB] [PDF] [CODE] |
| FedSplit: an algorithmic framework for fast federated optimization | Universidad de Berkeley | NeurIPS | 2020 | [PUB] [PDF] |
| Federated Bayesian Optimization via Thompson Sampling | NUS; MIT | NeurIPS | 2020 | [PUB] [PDF] [CODE] |
| Lower Bounds and Optimal Algorithms for Personalized Federated Learning | KAUST | NeurIPS | 2020 | [PUB] [PDF] |
| Robust Federated Learning: The Case of Affine Distribution Shifts | UC Santa Barbara; MIT | NeurIPS | 2020 | [PUB] [PDF] [CODE] |
| An Efficient Framework for Clustered Federated Learning | UC Berkeley; mente profunda | NeurIPS | 2020 | [PUB] [PDF] [CODE] |
| Distributionally Robust Federated Averaging | Pennsylvania State University | NeurIPS | 2020 | [PUB] [PDF] [CODE] |
| Personalized Federated Learning with Moreau Envelopes | La Universidad de Sídney | NeurIPS | 2020 | [PUB] [PDF] [CODE] |
| Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach | MIT; UT Austin | NeurIPS | 2020 | [PUB] [PDF] [UC.] |
| Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge | USC | NeurIPS | 2020 | [PUB] [PDF] [CODE] [解读] |
| Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization | CMU; Princeton | NeurIPS | 2020 | [PUB] [PDF] [CODE] [UC.] |
| Attack of the Tails: Yes, You Really Can Backdoor Federated Learning | Universidad de Wisconsin-Madison | NeurIPS | 2020 | [PUB] [PDF] |
| Federated Accelerated Stochastic Gradient Descent | stanford | NeurIPS | 2020 | [PUB] [PDF] [CODE] [VIDEO] |
| Inverting Gradients - How easy is it to break privacy in federated learning? | University of Siegen | NeurIPS | 2020 | [PUB] [PDF] [CODE] |
| Ensemble Distillation for Robust Model Fusion in Federated Learning | EPFL | NeurIPS | 2020 | [PUB] [PDF] [CODE] |
| Throughput-Optimal Topology Design for Cross-Silo Federated Learning | INRIA | NeurIPS | 2020 | [PUB] [PDF] [CODE] |
| Bayesian Nonparametric Federated Learning of Neural Networks | IBM | ICML | 2019 | [PUB] [PDF] [CODE] |
| Analyzing Federated Learning through an Adversarial Lens | Princeton; IBM | ICML | 2019 | [PUB] [PDF] [CODE] |
| Agnostic Federated Learning | ICML | 2019 | [PUB] [PDF] | |
| cpSGD: Communication-efficient and differentially-private distributed SGD | Princeton; Google | NeurIPS | 2018 | [PUB] [PDF] |
| Federated Multi-Task Learning | Stanford; USC; CMU | NeurIPS | 2017 | [PUB] [PDF] [CODE] |
Federated Learning papers accepted by top DM(Data Mining) conference and journal, Including KDD(ACM SIGKDD Conference on Knowledge Discovery and Data Mining) and WSDM(Web Search and Data Mining).
| Título | Afiliación | Evento | Año | Materiales |
|---|---|---|---|---|
| FedKDD: International Joint Workshop on Federated Learning for Data Mining and Graph Analytics | KDD Workshop | 2024 | [PUB] | |
| Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination | KDD | 2024 | [PUB] | |
| BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning | KDD | 2024 | [PUB] | |
| Federated Graph Learning with Structure Proxy Alignment | KDD | 2024 | [PUB] | |
| HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning | KDD | 2024 | [PUB] | |
| FedSecurity: A Benchmark for Attacks and Defenses in Federated Learning and Federated LLMs | KDD | 2024 | [PUB] | |
| Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization | KDD | 2024 | [PUB] | |
| FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning | KDD | 2024 | [PUB] | |
| On the Convergence of Zeroth-Order Federated Tuning for Large Language Models | KDD | 2024 | [PUB] | |
| CASA: Clustered Federated Learning with Asynchronous Clients | KDD | 2024 | [PUB] | |
| FLAIM: AIM-based Synthetic Data Generation in the Federated Setting | KDD | 2024 | [PUB] | |
| Privacy-Preserving Federated Learning using Flower Framework | KDD | 2024 | [PUB] | |
| FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning | KDD | 2024 | [PUB] | |
| FedNLR: Federated Learning with Neuron-wise Learning Rates | KDD | 2024 | [PUB] | |
| FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model | KDD | 2024 | [PUB] | |
| FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation | KDD | 2024 | [PUB] | |
| Preventing Strategic Behaviors in Collaborative Inference for Vertical Federated Learning | KDD | 2024 | [PUB] | |
| PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection | KDD | 2024 | [PUB] | |
| FedRoLA: Robust Federated Learning Against Model Poisoning via Layer-based Aggregation | KDD | 2024 | [PUB] | |
| FedGTP: Exploiting Inter-Client Spatial Dependency in Federated Graph-based Traffic Prediction | KDD | 2024 | [PUB] | |
| OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning | KDD | 2024 | [PUB] | |
| Personalized Federated Continual Learning via Multi-Granularity Prompt | KDD | 2024 | [PUB] | |
| Enabling Collaborative Test-Time Adaptation in Dynamic Environment via Federated Learning | KDD | 2024 | [PUB] | |
| GPFedRec: Graph-Guided Personalization for Federated Recommendation | KDD | 2024 | [PUB] | |
| Asynchronous Vertical Federated Learning for Kernelized AUC Maximization | KDD | 2024 | [PUB] | |
| VertiMRF: Differentially Private Vertical Federated Data Synthesis | KDD | 2024 | [PUB] | |
| User Consented Federated Recommender System Against Personalized Attribute Inference Attack | HKUST | WSDM | 2024 | [PUB] [PDF] [CODE] |
| Guardian: Guarding against Gradient Leakage with Provable Defense for Federated Learning | ECNU | WSDM | 2024 | [PUB] |
| Privacy Matters: Vertical Federated Linear Contextual Bandits for Privacy Protected Recommendation | Universidad de Cambridge | KDD | 2023 | [PUB] [PDF] |
| FedDefender: Client-Side Attack-Tolerant Federated Learning | KAIST | KDD | 2023 | [PUB] [PDF] [CODE] |
| FedAPEN: Personalized Cross-silo Federated Learning with Adaptability to Statistical Heterogeneity | ZJU | KDD | 2023 | [PUB] [CODE] |
| FedPseudo: Privacy-Preserving Pseudo Value-Based Deep Learning Models for Federated Survival Analysis | UMBC | KDD | 2023 | [PUB] [PDF] |
| ShapleyFL: Robust Federated Learning Based on Shapley Value | ZJU | KDD | 2023 | [PUB] [CODE] |
| Federated Few-shot Learning | Universidad de Virginia | KDD | 2023 | [PUB] [PDF] [CODE] |
| Theoretical Convergence Guaranteed Resource-Adaptive Federated Learning with Mixed Heterogeneity | SDU | KDD | 2023 | [PUB] |
| Personalized Federated Learning with Parameter Propagation | UIUC | KDD | 2023 | [PUB] |
| Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining | universidad de pittsburgh | KDD | 2023 | [PUB] [PDF] [CODE] |
| CriticalFL: A Critical Learning Periods Augmented Client Selection Framework for Efficient Federated Learning | SUNY-Binghamton University | KDD | 2023 | [PUB] [PDF] |
| FLAMES2Graph: An Interpretable Federated Multivariate Time Series Classification Framework | L3S Research Center | KDD | 2023 | [PUB] [PDF] |
| FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy | SJTU | KDD | 2023 | [PUB] [PDF] [CODE] |
| Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework | UCSD | KDD | 2023 | [PUB] [PDF] [CODE] |
| DM-PFL: Hitchhiking Generic Federated Learning for Efficient Shift-Robust Personalization | BUAA | KDD | 2023 | [PUB] [CODE] |
| FS-REAL: Towards Real-World Cross-Device Federated Learning | Grupo Alibaba | KDD | 2023 | [PUB] [PDF] |
| FedMultimodal: A Benchmark for Multimodal Federated Learning | USC | KDD | 2023 | [PUB] [PDF] [CODE] |
| PrivateRec: Differentially Private Model Training and Online Serving for Federated News Recommendation | RUC | KDD | 2023 | [PUB] [PDF] [NEWS] |
| Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks | HKUST; Grupo Alibaba | KDD | 2023 | [PUB] [PDF] [CODE] |
| UA-FedRec: Untargeted Attack on Federated News Recommendation | USTC | KDD | 2023 | [PUB] [PDF] [CODE] |
| International Workshop on Federated Learning for Distributed Data Mining | Universidad Estatal de Michigan | KDD Workshop Summaries | 2023 | [PUB] [PAGE] |
| Is Normalization Indispensable for Multi-domain Federated Learning? | KDD workshop | 2023 | [PUB] | |
| Distributed Personalized Empirical Risk Minimization. | KDD workshop | 2023 | [PUB] | |
| Once-for-All Federated Learning: Learning From and Deploying to Heterogeneous Clients. | KDD workshop | 2023 | [PUB] | |
| SparseVFL: Communication-Efficient Vertical Federated Learning Based on Sparsification of Embeddings and Gradients. | KDD workshop | 2023 | [PUB] | |
| Optimization of User Resources in Federated Learning for Urban Sensing Applications | KDD workshop | 2023 | [PUB] | |
| FedLEGO: Enabling Heterogenous Model Cooperation via Brick Reassembly in Federated Learning. | KDD workshop | 2023 | [PUB] | |
| Federated Graph Analytics with Differential Privacy. | KDD workshop | 2023 | [PUB] | |
| Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing. | KDD workshop | 2023 | [PUB] | |
| Uncertainty Quantification in Federated Learning for Heterogeneous Health Data | KDD workshop | 2023 | [PUB] | |
| A Systematic Evaluation of Federated Learning on Biomedical Natural Language Processing. | KDD workshop | 2023 | [PUB] | |
| Taming Heterogeneity to Deal with Test-Time Shift in Federated Learning. | KDD workshop | 2023 | [PUB] | |
| Federated Blood Supply Chain Demand Forecasting: A Case Study. | KDD workshop | 2023 | [PUB] | |
| Stochastic Clustered Federated Learning. | KDD workshop | 2023 | [PUB] | |
| A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection. | KDD workshop | 2023 | [PUB] | |
| Exploring the Efficacy of Data-Decoupled Federated Learning for Image Classification and Medical Imaging Analysis. | KDD workshop | 2023 | [PUB] | |
| FedNoisy: A Federated Noisy Label Learning Benchmark | KDD workshop | 2023 | [PUB] | |
| Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging | KDD workshop | 2023 | [PUB] | |
| Federated learning for competing risk analysis in healthcare. | KDD workshop | 2023 | [PUB] | |
| Federated Threat Detection for Smart Home IoT rules. | KDD workshop | 2023 | [PUB] | |
| Federated Unlearning for On-Device Recommendation | UQ | WSDM | 2023 | [PUB] [PDF] |
| Collaboration Equilibrium in Federated Learning | JUE | KDD | 2022 | [PUB] [PDF] [CODE] |
| Connected Low-Loss Subspace Learning for a Personalization in Federated Learning | Ulsan National Institute of Science and Technology | KDD | 2022 | [PUB] [PDF] [CODE] |
| FedMSplit: Correlation-Adaptive Federated Multi-Task Learning across Multimodal Split Networks | Universidad de Virginia | KDD | 2022 | [PUB] |
| Communication-Efficient Robust Federated Learning with Noisy Labels | universidad de pittsburgh | KDD | 2022 | [PUB] [PDF] |
| FLDetector: Detecting Malicious Clients in Federated Learning via Checking Model-Updates Consistency | USTC | KDD | 2022 | [PUB] [PDF] [CODE] |
| Practical Lossless Federated Singular Vector Decomposition Over Billion-Scale Data | HKUST | KDD | 2022 | [PUB] [PDF] [CODE] |
| FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy | SJTU | KDD | 2022 | [PUB] [PDF] |
| FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Platform for Federated Graph Learning | Alibaba | KDD (Best Paper Award) | 2022 | [PUB] [PDF] [CODE] |
| Fed-LTD: Towards Cross-Platform Ride Hailing via Federated Learning to Dispatch | BUAA | KDD | 2022 | [PUB] [PDF] [解读] |
| Felicitas: Federated Learning in Distributed Cross Device Collaborative Frameworks | USTC | KDD | 2022 | [PUB] [PDF] |
| No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices | Renmin University of China | KDD | 2022 | [PUB] [PDF] |
| FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling | JUE | KDD | 2022 | [PUB] [PDF] [CODE] |
| PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion | The University of Queensland | WSDM | 2022 | [PUB] [PDF] |
| Fed2: Feature-Aligned Federated Learning | George Mason University; Microsoft; University of Maryland | KDD | 2021 | [PUB] [PDF] |
| FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data | Universidad de Nankín | KDD | 2021 | [PUB] [CODE] |
| Federated Adversarial Debiasing for Fair and Trasnferable Representations | Universidad Estatal de Michigan | KDD | 2021 | [PUB] [PAGE] [CODE] [SLIDE] |
| Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling | USC | KDD | 2021 | [PUB] [CODE] [解读] |
| AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization | Xidian University;JD Tech | KDD | 2021 | [PUB] [PDF] |
| FLOP: Federated Learning on Medical Datasets using Partial Networks | Universidad de Duke | KDD | 2021 | [PUB] [PDF] [CODE] |
| A Practical Federated Learning Framework for Small Number of Stakeholders | ETH Zürich | WSDM | 2021 | [PUB] [CODE] |
| Federated Deep Knowledge Tracing | USTC | WSDM | 2021 | [PUB] [CODE] |
| FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems | Colegio Universitario de Dublín | KDD | 2020 | [PUB] [VIDEO] |
| Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data | JD Tech | KDD | 2020 | [PUB] [PDF] [VIDEO] |
| Federated Online Learning to Rank with Evolution Strategies | Facebook AI Research | WSDM | 2019 | [PUB] [CODE] |
Federated Learning papers accepted by top Secure conference and journal, Including S&P(IEEE Symposium on Security and Privacy), CCS(Conference on Computer and Communications Security), USENIX Security(Usenix Security Symposium) and NDSS(Network and Distributed System Security Symposium).
| Título | Afiliación | Evento | Año | Materiales |
|---|---|---|---|---|
| Byzantine-Robust Decentralized Federated Learning | CCS | 2024 | [PUB] | |
| Not One Less: Exploring Interplay between User Profiles and Items in Untargeted Attacks against Federated Recommendation | CCS | 2024 | [PUB] | |
| Cross-silo Federated Learning with Record-level Personalized Differential Privacy. | CCS | 2024 | [PUB] | |
| Samplable Anonymous Aggregation for Private Federated Data Analysis | CCS | 2024 | [PUB] | |
| Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential Privacy | CCS | 2024 | [PUB] | |
| Distributed Backdoor Attacks on Federated Graph Learning and Certified Defenses | CCS | 2024 | [PUB] | |
| Two-Tier Data Packing in RLWE-based Homomorphic Encryption for Secure Federated Learning. | CCS | 2024 | [PUB] | |
| Poster: Protection against Source Inference Attacks in Federated Learning using Unary Encoding and Shuffling. | CCS | 2024 | [PUB] | |
| Poster: End-to-End Privacy-Preserving Vertical Federated Learning using Private Cross-Organizational Data Collaboration. | CCS | 2024 | [PUB] | |
| FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting | NDSS | 2024 | [PUB] | |
| FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning | NDSS | 2024 | [PUB] | |
| Automatic Adversarial Adaption for Stealthy Poisoning Attacks in Federated Learning | NDSS | 2024 | [PUB] | |
| CrowdGuard: Federated Backdoor Detection in Federated Learning | NDSS | 2024 | [PUB] | |
| Protecting Label Distribution in Cross-Silo Federated Learning | S&P | 2024 | [PUB] | |
| FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks | S&P | 2024 | [PUB] | |
| BadVFL: Backdoor Attacks in Vertical Federated Learning | S&P | 2024 | [PUB] | |
| SHERPA: Explainable Robust Algorithms for Privacy-Preserved Federated Learning in Future Networks to Defend Against Data Poisoning Attacks | S&P | 2024 | [PUB] | |
| Loki: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation | S&P | 2024 | [PUB] | |
| Poster: Towards Privacy-Preserving Federated Recommendation via Synthetic Interactions. | S&P Workshop | 2024 | [PUB] | |
| A Performance Analysis for Confidential Federated Learning. | S&P Workshop | 2024 | [PUB] | |
| Turning Privacy-preserving Mechanisms against Federated Learning | Universidad de Pavía | CCS | 2023 | [PUB] [PDF] |
| MESAS: Poisoning Defense for Federated Learning Resilient against Adaptive Attackers | Universidad de Wurzburgo | CCS | 2023 | [PUB] |
| martFL: Enabling Utility-Driven Data Marketplace with a Robust and Verifiable Federated Learning Architecture | JUE | CCS | 2023 | [PUB] [PDF] [CODE] |
| Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks | UIUC | CCS | 2023 | [PUB] [PDF] |
| Poster: Verifiable Data Valuation with Strong Fairness in Horizontal Federated Learning | NSYSU | CCS | 2023 | [PUB] |
| Poster: Bridging Trust Gaps: Data Usage Transparency in Federated Data Ecosystems | RWTH Aachen University | CCS | 2023 | [PUB] |
| Every Vote Counts: Ranking-Based Training of Federated Learning to Resist Poisoning Attacks | Universidad de Massachusetts Amherst | USENIX Security | 2023 | [PUB] [PDF] |
| PrivateFL: Accurate, Differentially Private Federated Learning via Personalized Data Transformation | JHU | USENIX Security | 2023 | [PUB] [CODE] |
| Gradient Obfuscation Gives a False Sense of Security in Federated Learning | NCSU | USENIX Security | 2023 | [PUB] [PDF] [CODE] |
| FedVal: Different good or different bad in federated learning | AI Sweden | USENIX Security | 2023 | [PUB] [PDF] [CODE] |
| Securing Federated Sensitive Topic Classification against Poisoning Attacks | IMDEA Networks Institute | NDSS | 2023 | [PUB] [PDF] [CODE] |
| PPA: Preference Profiling Attack Against Federated Learning | NJUST | NDSS | 2023 | [PUB] [PDF] |
| Turning Privacy-preserving Mechanisms against Federated Learning | University of Pavia; TU Delft; University of Padua; Radboud University | CCS | 2023 | [PUB] [PDF] [CODE] |
| CERBERUS: Exploring Federated Prediction of Security Events | UCL London | CCS | 2022 | [PUB] [PDF] |
| EIFFeL: Ensuring Integrity for Federated Learning | UW-Madison | CCS | 2022 | [PUB] [PDF] |
| Eluding Secure Aggregation in Federated Learning via Model Inconsistency | SPRING Lab; EPFL | CCS | 2022 | [PUB] [PDF] [CODE] |
| Federated Boosted Decision Trees with Differential Privacy | Universidad de Warwick | CCS | 2022 | [PUB] [PDF] [CODE] |
| FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information | Universidad de Duke | S&P | 2023 | [PUB] [PDF] |
| Scalable and Privacy-Preserving Federated Principal Component Analysis | EPFL; Tune Insight SA | S&P | 2023 | [PUB] [PDF] |
| SafeFL: MPC-friendly Framework for Private and Robust Federated Learning | TU Darmstadt | S&P Workshop | 2023 | [PUB] |
| On the Pitfalls of Security Evaluation of Robust Federated Learning. | umass | S&P Workshop | 2023 | [PUB] |
| BayBFed: Bayesian Backdoor Defense for Federated Learning | TU Darmstadt; UTSA | S&P | 2023 | [PUB] [PDF] |
| 3DFed: Adaptive and Extensible Framework for Covert Backdoor Attack in Federated Learning | PolyU | S&P | 2023 | [PUB] [CODE] |
| RoFL: Robustness of Secure Federated Learning. | ETH Zúrich | S&P | 2023 | [PUB] [PDF] [CODE] |
| Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning. | upenn | S&P | 2023 | [PUB] [CODE] |
| ELSA: Secure Aggregation for Federated Learning with Malicious Actors. | S&P | 2023 | ||
| Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy | Universidad de Fudan | S&P | 2023 | [PUB] [PDF] |
| Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning | University of Massachusetts | S&P | 2022 | [PUB] [VIDEO] |
| SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost | Investigación de Microsoft | USENIX Security | 2022 | [PUB] [PDF] [CODE] [VIDEO] [SUPP] |
| Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors | University of Vermont | USENIX Security | 2022 | [PUB] [SLIDE] [VIDEO] |
| Label Inference Attacks Against Vertical Federated Learning | ZJU | USENIX Security | 2022 | [PUB] [SLIDE] [CODE] [VIDEO] |
| FLAME: Taming Backdoors in Federated Learning | Technical University of Darmstadt | USENIX Security | 2022 | [PUB] [SLIDE] [PDF] [VIDEO] |
| Local and Central Differential Privacy for Robustness and Privacy in Federated Learning | University at Buffalo, SUNY | NDSS | 2022 | [PUB] [PDF] [VIDEO] [UC.] |
| Interpretable Federated Transformer Log Learning for Cloud Threat Forensics | University of the Incarnate Word | NDSS | 2022 | [PUB] [VIDEO] [UC.] |
| FedCRI: Federated Mobile Cyber-Risk Intelligence | Technical University of Darmstadt | NDSS | 2022 | [PUB] [VIDEO] |
| DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection | Technical University of Darmstadt | NDSS | 2022 | [PUB] [PDF] [VIDEO] |
| Private Hierarchical Clustering in Federated Networks | NUES | CCS | 2021 | [PUB] [PDF] |
| FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping | Universidad de Duke | NDSS | 2021 | [PUB] [PDF] [CODE] [VIDEO] [SLIDE] |
| POSEIDON: Privacy-Preserving Federated Neural Network Learning | EPFL | NDSS | 2021 | [PUB] [VIDEO] |
| Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning | Universidad de Massachusetts Amherst | NDSS | 2021 | [PUB] [CODE] [VIDEO] |
| SAFELearn: Secure Aggregation for private FEderated Learning | TU Darmstadt | S&P Workshop | 2021 | [PUB] |
| Local Model Poisoning Attacks to Byzantine-Robust Federated Learning | La Universidad Estatal de Ohio | USENIX Security | 2020 | [PUB] [PDF] [CODE] [VIDEO] [SLIDE] |
| A Reliable and Accountable Privacy-Preserving Federated Learning Framework using the Blockchain | Universidad de Kansas | CCS (Poster) | 2019 | [PUB] |
| IOTFLA : A Secured and Privacy-Preserving Smart Home Architecture Implementing Federated Learning | Université du Québéc á Montréal | S&P Workshop | 2019 | [PUB] |
| Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning | Universidad de Massachusetts Amherst | S&P | 2019 | [PUB] [VIDEO] [SLIDE] [CODE] |
| Practical Secure Aggregation for Privacy Preserving Machine Learning | CCS | 2017 | [PUB] [PDF] [解读] [UC.] [UC] |
Federated Learning papers accepted by top CV(computer vision) conference and journal, Including CVPR(Computer Vision and Pattern Recognition), ICCV(IEEE International Conference on Computer Vision), ECCV(European Conference on Computer Vision), MM(ACM International Conference on Multimedia), IJCV(International Journal of Computer Vision).
| Título | Afiliación | Evento | Año | Materiales |
|---|---|---|---|---|
| DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations | MM | 2024 | [PUB] | |
| One-shot-but-not-degraded Federated Learning | MM | 2024 | [PUB] | |
| Overcoming Spatial-Temporal Catastrophic Forgetting for Federated Class-Incremental Learning | MM | 2024 | [PUB] | |
| FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models | MM | 2024 | [PUB] | |
| Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-rank Decomposition | MM | 2024 | [PUB] | |
| CoAst: Validation-Free Contribution Assessment for Federated Learning based on Cross-Round Valuation | MM | 2024 | [PUB] | |
| Spatio-temporal Heterogeneous Federated Learning for Time Series Classification with Multi-view Orthogonal Training | MM | 2024 | [PUB] | |
| FedEvalFair: A Privacy-Preserving and Statistically Grounded Federated Fairness Evaluation Framework | MM | 2024 | [PUB] | |
| One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity | MM | 2024 | [PUB] | |
| FedSLS: Exploring Federated Aggregation in Saliency Latent Space | MM | 2024 | [PUB] | |
| Cluster-driven Personalized Federated Recommendation with Interest-aware Graph Convolution Network for Multimedia | MM | 2024 | [PUB] | |
| FedBCGD: Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning | MM | 2024 | [PUB] | |
| Federated Morozov Regularization for Shortcut Learning in Privacy Preserving Learning with Watermarked Image Data | MM | 2024 | [PUB] | |
| Cross-Modal Meta Consensus for Heterogeneous Federated Learning | MM | 2024 | [PUB] | |
| Masked Random Noise for Communication-Efficient Federated Learning | MM | 2024 | [PUB] | |
| Heterogeneity-Aware Federated Deep Multi-View Clustering towards Diverse Feature Representations | MM | 2024 | [PUB] | |
| Adaptive Hierarchical Aggregation for Federated Object Detection | MM | 2024 | [PUB] | |
| FedCAFE: Federated Cross-Modal Hashing with Adaptive Feature Enhancement | MM | 2024 | [PUB] | |
| Federated Fuzzy C-means with Schatten-p Norm Minimization | MM | 2024 | [PUB] | |
| Towards Effective Federated Graph Anomaly Detection via Self-boosted Knowledge Distillation | MM | 2024 | [PUB] | |
| Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification | IJCV | 2024 | [PUB] | |
| FedHide: Federated Learning by Hiding in the Neighbors | ECCV | 2024 | [PUB] | |
| FedVAD: Enhancing Federated Video Anomaly Detection with GPT-Driven Semantic Distillation | ECCV | 2024 | [PUB] | |
| FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients | ECCV | 2024 | [PUB] | |
| Pick-a-Back: Selective Device-to-Device Knowledge Transfer in Federated Continual Learning | ECCV | 2024 | [PUB] | |
| Federated Learning with Local Openset Noisy Labels | ECCV | 2024 | [PUB] | |
| FedTSA: A Cluster-Based Two-Stage Aggregation Method for Model-Heterogeneous Federated Learning. | ECCV | 2024 | [PUB] | |
| Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality Selection | ECCV | 2024 | [PUB] | |
| BAFFLE: A Baseline of Backpropagation-Free Federated Learning | ECCV | 2024 | [PUB] | |
| PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learning | ECCV | 2024 | [PUB] | |
| Fisher Calibration for Backdoor-Robust Heterogeneous Federated Learning | ECCV | 2024 | [PUB] | |
| Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents | ECCV | 2024 | [PUB] | |
| FedHARM: Harmonizing Model Architectural Diversity in Federated Learning | ECCV | 2024 | [PUB] | |
| SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-device Inference. | ECCV | 2024 | [PUB] | |
| Personalized Federated Domain-Incremental Learning Based on Adaptive Knowledge Matching. | ECCV | 2024 | [PUB] | |
| Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning | ECCV | 2024 | [PUB] | |
| Towards Multi-modal Transformers in Federated Learning | ECCV | 2024 | [PUB] | |
| Local and Global Flatness for Federated Domain Generalization | ECCV | 2024 | [PUB] | |
| Feature Diversification and Adaptation for Federated Domain Generalization | ECCV | 2024 | [PUB] | |
| PFEDEDIT: Personalized Federated Learning via Automated Model Editing | ECCV | 2024 | [PUB] | |
| FedHCA2: Towards Hetero-Client Federated Multi-Task Learning | SJTU | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity | ¿QUÉ? | CVPR | 2024 | [PUB] [PDF] [CODE] |
| Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts | NWPU; HKUST | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| FedMef: Towards Memory-efficient Federated Dynamic Pruning | CUHK | CVPR | 2024 | [PUB] [SUPP] [PDF] |
| Communication-Efficient Federated Learning with Accelerated Client Gradient | SNU | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space | IITH | CVPR | 2024 | [PUB] [SUPP] [CODE] |
| Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning | TJUT | CVPR | 2024 | [PUB] [SUPP] [CODE] |
| Towards Efficient Replay in Federated Incremental Learning | FOTOS | CVPR | 2024 | [PUB] [SUPP] [PDF] |
| Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices | Utah | CVPR | 2024 | [PUB] [SUPP] [PDF] |
| Data Valuation and Detections in Federated Learning | NUES | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| Decentralized Directed Collaboration for Personalized Federated Learning | NJUST | CVPR | 2024 | [PUB] [SUPP] [PDF] |
| Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning | UBC | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| Global and Local Prompts Cooperation via Optimal Transport for Federated Learning | ShanghaiTech University | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data | ZJU | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| Relaxed Contrastive Learning for Federated Learning | SNU | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning | Purdue University | CVPR | 2024 | [PUB] [SUPP] [PDF] [VIDEO] |
| Traceable Federated Continual Learning | BUPT | CVPR | 2024 | [PUB] [SUPP] [CODE] |
| Federated Online Adaptation for Deep Stereo | University of Bologna | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] [PAGE] [VIDEO] |
| Federated Generalized Category Discovery | UniTn | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization | DAKOTA DEL NORTE | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| Text-Enhanced Data-free Approach for Federated Class-Incremental Learning | Monash University | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees | UIUC; Nvidia | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning | KAIST | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| FedUV: Uniformity and Variance for Heterogeneous Federated Learning | Universidad de Davis | CVPR | 2024 | [PUB] [SUPP] [PDF] |
| FedAS: Bridging Inconsistency in Personalized Federated Learning | ¿QUÉ? | CVPR | 2024 | [PUB] [CODE] |
| FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning | Lapis Labs | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| Device-Wise Federated Network Pruning | PITT | CVPR | 2024 | [PUB] [SUPP] |
| Byzantine-robust Decentralized Federated Learning via Dual-domain Clustering and Trust Bootstrapping | HNU; PolyU; AIRES | CVPR | 2024 | [PUB] [SUPP] |
| DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning | HKUST; PolyU | CVPR | 2024 | [PUB] [SUPP] [PDF] |
| An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning | SJTU | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] [POSTER] [SLIDES] |
| An Aggregation-Free Federated Learning for Tackling Data Heterogeneity | A* STAR | CVPR | 2024 | [PUB] [SUPP] [PDF] |
| FLHetBench: Benchmarking Device and State Heterogeneity in Federated Learning | BUAA; HKU | CVPR | 2024 | [PUB] [SUPP] [CODE] [PAGE] [POSTER] [VIDEO] |
| Collaborative Visual Place Recognition through Federated Learning | CVPR workshop | 2024 | [PUB] [SUPP] [PDF] | |
| FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer | CVPR workshop | 2024 | [PUB] [SUPP] [PDF] | |
| Federated Hyperparameter Optimization Through Reward-Based Strategies: Challenges and Insights | CVPR workshop | 2024 | [PUB] | |
| On the Efficiency of Privacy Attacks in Federated Learning | CVPR workshop | 2024 | [PUB] [PDF] | |
| FedCE: Personalized Federated Learning Method based on Clustering Ensembles | BJTU | MM | 2023 | [PUB] |
| FedVQA: Personalized Federated Visual Question Answering over Heterogeneous Scenes | Leiden University | MM | 2023 | [PUB] |
| Towards Fast and Stable Federated Learning: Confronting Heterogeneity via Knowledge Anchor | XJTU | MM | 2023 | [PUB] [PDF] [CODE] |
| Federated Deep Multi-View Clustering with Global Self-Supervision | UESTC | MM | 2023 | [PUB] [PDF] |
| FedAA: Using Non-sensitive Modalities to Improve Federated Learning while Preserving Image Privacy | ZJU | MM | 2023 | [PUB] |
| Prototype-guided Knowledge Transfer for Federated Unsupervised Cross-modal Hashing | SDNU | MM | 2023 | [PUB] [CODE] |
| Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data | ZJU | MM | 2023 | [PUB] [PDF] |
| FedCD: A Classifier Debiased Federated Learning Framework for Non-IID Data | BUPT | MM | 2023 | [PUB] |
| Federated Learning with Label-Masking Distillation | UCAS | MM | 2023 | [PUB] [CODE] |
| Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data | SDU | MM | 2023 | [PUB] [PDF] [CODE] |
| A Four-Pronged Defense Against Byzantine Attacks in Federated Learning | FOTOS | MM | 2023 | [PUB] [PDF] |
| Client-Adaptive Cross-Model Reconstruction Network for Modality-Incomplete Multimodal Federated Learning | CAS; Peng Cheng Laboratory; UCAS | MM | 2023 | [PUB] |
| FedGH: Heterogeneous Federated Learning with Generalized Global Header | NKU | MM | 2023 | [PUB] [PDF] [CODE] |
| Cuing Without Sharing: A Federated Cued Speech Recognition Framework via Mutual Knowledge Distillation | CUHK | MM | 2023 | [PUB] [PDF] [CODE] |
| AffectFAL: Federated Active Affective Computing with Non-IID Data | TJUT | MM | 2023 | [PUB] [CODE] |
| Improving Federated Person Re-Identification through Feature-Aware Proximity and Aggregation | SZU | MM | 2023 | [PUB] |
| Towards Attack-tolerant Federated Learning via Critical Parameter Analysis | KAIST | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generation | NTU; Nvidia | ICCV | 2023 | [PUB] [PDF] [SUPP] |
| Generative Gradient Inversion via Over-Parameterized Networks in Federated Learning | A*STAR | ICCV | 2023 | [PUB] [CODE] [SUPP] |
| GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning | SJTU | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| Workie-Talkie: Accelerating Federated Learning by Overlapping Computing and Communications via Contrastive Regularization | University of Houston | ICCV | 2023 | [PUB] [SUPP] |
| PGFed: Personalize Each Client's Global Objective for Federated Learning | universidad de pittsburgh | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learning | UCF | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning | TCL AI Lab | ICCV | 2023 | [PUB] [PDF] [SUPP] |
| FedPD: Federated Open Set Recognition with Parameter Disentanglement | City University of Hong Kong | ICCV | 2023 | [PUB] [CODE] |
| TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation | ETH Zurich; Sony AI | ICCV | 2023 | [PUB] [PDF] [CODE] |
| Towards Instance-adaptive Inference for Federated Learning | A*STAR | ICCV | 2023 | [PUB] [PDF] [CODE] |
| Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence | SCU; Engineering Research Center of Machine Learning and Industry Intelligence | ICCV | 2023 | [PUB] [PDF] [CODE] |
| zPROBE: Zero Peek Robustness Checks for Federated Learning | Purdue University | ICCV | 2023 | [PUB] [PDF] [SUPP] |
| ProtoFL: Unsupervised Federated Learning via Prototypical Distillation | KakaoBank Corp. | ICCV | 2023 | [PUB] [PDF] |
| MAS: Towards Resource-Efficient Federated Multiple-Task Learning | Sony AI | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation | PKU | ICCV | 2023 | [PUB] [PDF] [SUPP] |
| When Do Curricula Work in Federated Learning? | UCSD | ICCV | 2023 | [PUB] [PDF] [SUPP] |
| Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples | Universidad de Duke | ICCV | 2023 | [PUB] [PDF] [CODE] |
| Multi-Metrics Adaptively Identifies Backdoors in Federated Learning | RABO | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier | ZJU | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation | Ludwig Maximilian University of Munich; Siemens Technology | ICCV | 2023 | [PUB] [PDF] [SUPP] |
| Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration | BUAA | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| Global Balanced Experts for Federated Long-Tailed Learning | CUHK-Shenzhen | ICCV | 2023 | [PUB] [CODE] [SUPP] |
| Knowledge-Aware Federated Active Learning with Non-IID Data | La Universidad de Sídney | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| Enhancing Privacy Preservation in Federated Learning via Learning Rate Perturbation | BUPT | ICCV | 2023 | [PUB] [SUPP] |
| Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels | CMU | ICCV | 2023 | [PUB] [PDF] [SUPP] |
| Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beat | Rice University | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| Robust Heterogeneous Federated Learning under Data Corruption | ¿QUÉ? | ICCV | 2023 | [PUB] [CODE] [SUPP] |
| Personalized Semantics Excitation for Federated Image Classification | Universidad de Tulane | ICCV | 2023 | [PUB] [CODE] |
| Reducing Training Time in Cross-Silo Federated Learning Using Multigraph Topology | AIOZ | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| Window-based Model Averaging Improves Generalization in Heterogeneous Federated Learning. | Politecnico di Torino | ICCV workshop | 2023 | [PUB] [PDF] |
| Experience Replay as an Effective Strategy for Optimizing Decentralized Federated Learning. | University of Catania | ICCV workshop | 2023 | [PUB] |
| FedRCIL: Federated Knowledge Distillation for Representation based Contrastive Incremental Learning. | Centre for Research and Technology Hellas; University of West Attica | ICCV workshop | 2023 | [PUB] [CODE] |
| FedLID: Self-Supervised Federated Learning for Leveraging Limited Image Data. | Centre for Research and Technology Hellas; University of West Attica | ICCV workshop | 2023 | [PUB] |
| Rethinking Federated Learning With Domain Shift: A Prototype View | ¿QUÉ? | CVPR | 2023 | [PUB] [CODE] |
| Class Balanced Adaptive Pseudo Labeling for Federated Semi-Supervised Learning | ECNU | CVPR | 2023 | [PUB] [CODE] |
| DaFKD: Domain-Aware Federated Knowledge Distillation | FOTOS | CVPR | 2023 | [PUB] [CODE] |
| The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning | Purdue University | CVPR | 2023 | [PUB] [PDF] |
| FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation | ZJU | CVPR | 2023 | [PUB] |
| On the Effectiveness of Partial Variance Reduction in Federated Learning With Heterogeneous Data | DTU | CVPR | 2023 | [PUB] [PDF] |
| Elastic Aggregation for Federated Optimization | meituán | CVPR | 2023 | [PUB] |
| FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning | UCLA | CVPR | 2023 | [PUB] [PDF] |
| Adaptive Channel Sparsity for Federated Learning Under System Heterogeneity | MU | CVPR | 2023 | [PUB] |
| ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous Clients | GaTech | CVPR | 2023 | [PUB] [CODE] |
| Reliable and Interpretable Personalized Federated Learning | TJU | CVPR | 2023 | [PUB] |
| Federated Domain Generalization With Generalization Adjustment | SJTU | CVPR | 2023 | [PUB] [CODE] |
| Make Landscape Flatter in Differentially Private Federated Learning | JUE | CVPR | 2023 | [PUB] [PDF] [CODE] |
| Confidence-Aware Personalized Federated Learning via Variational Expectation Maximization | KU Lovaina | CVPR | 2023 | [PUB] [PDF] [CODE] |
| STDLens: Model Hijacking-Resilient Federated Learning for Object Detection | GaTech | CVPR | 2023 | [PUB] [PDF] [CODE] |
| Re-Thinking Federated Active Learning Based on Inter-Class Diversity | KAIST | CVPR | 2023 | [PUB] [PDF] [CODE] |
| Learning Federated Visual Prompt in Null Space for MRI Reconstruction | A*STAR | CVPR | 2023 | [PUB] [PDF] [CODE] |
| Fair Federated Medical Image Segmentation via Client Contribution Estimation | CUHK | CVPR | 2023 | [PUB] [PDF] [CODE] |
| Federated Learning With Data-Agnostic Distribution Fusion | NJU | CVPR | 2023 | [PUB] [CODE] |
| How To Prevent the Poor Performance Clients for Personalized Federated Learning? | CSU | CVPR | 2023 | [PUB] |
| GradMA: A Gradient-Memory-Based Accelerated Federated Learning With Alleviated Catastrophic Forgetting | ECNU | CVPR | 2023 | [PUB] [PDF] [CODE] |
| Bias-Eliminating Augmentation Learning for Debiased Federated Learning | UNT | CVPR | 2023 | [PUB] |
| Federated Incremental Semantic Segmentation | CAS; UCAS | CVPR | 2023 | [PUB] [PDF] [CODE] |
| Asynchronous Federated Continual Learning | University of Padova | CVPR workshop | 2023 | [PUB] [PDF] [SILDES] [CODE] |
| Mixed Quantization Enabled Federated Learning To Tackle Gradient Inversion Attacks | UMBC | CVPR workshop | 2023 | [PUB] [CODE] |
| OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework | meituán | CVPR workshop | 2023 | [PUB] [PDF] [CODE] |
| Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Data | utexas | CVPR workshop | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| TimelyFL: Heterogeneity-Aware Asynchronous Federated Learning With Adaptive Partial Training | USC | CVPR workshop | 2023 | [PUB] [PDF] |
| Many-Task Federated Learning: A New Problem Setting and a Simple Baseline | utexas | CVPR workshop | 2023 | [PUB] [CODE] |
| Confederated Learning: Going Beyond Centralization | CAS; UCAS | MM | 2022 | [PUB] |
| Few-Shot Model Agnostic Federated Learning | ¿QUÉ? | MM | 2022 | [PUB] [CODE] |
| Feeling Without Sharing: A Federated Video Emotion Recognition Framework Via Privacy-Agnostic Hybrid Aggregation | TJUT | MM | 2022 | [PUB] |
| FedLTN: Federated Learning for Sparse and Personalized Lottery Ticket Networks | ECCV | 2022 | [PUB] [SUPP] | |
| Auto-FedRL: Federated Hyperparameter Optimization for Multi-Institutional Medical Image Segmentation | ECCV | 2022 | [PUB] [SUPP] [PDF] [CODE] | |
| Improving Generalization in Federated Learning by Seeking Flat Minima | Politecnico di Torino | ECCV | 2022 | [PUB] [SUPP] [PDF] [CODE] |
| AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation | ECCV | 2022 | [PUB] [SUPP] [PDF] [CODE] [PAGE] | |
| SphereFed: Hyperspherical Federated Learning | ECCV | 2022 | [PUB] [SUPP] [PDF] | |
| Federated Self-Supervised Learning for Video Understanding | ECCV | 2022 | [PUB] [PDF] [CODE] | |
| FedVLN: Privacy-Preserving Federated Vision-and-Language Navigation | ECCV | 2022 | [PUB] [SUPP] [PDF] [CODE] | |
| Addressing Heterogeneity in Federated Learning via Distributional Transformation | ECCV | 2022 | [PUB] [CODE] | |
| FedX: Unsupervised Federated Learning with Cross Knowledge Distillation | KAIST | ECCV | 2022 | [PUB] [SUPP] [PDF] [CODE] |
| Personalizing Federated Medical Image Segmentation via Local Calibration | Xiamen University | ECCV | 2022 | [PUB] [SUPP] [PDF] [CODE] |
| ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework | GOLPEAR | CVPR | 2022 | [PUB] |
| Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning | stanford | CVPR | 2022 | [PUB] [SUPP] [PDF] [CODE] [VIDEO] |
| FedCorr: Multi-Stage Federated Learning for Label Noise Correction | Singapore University of Technology and Design | CVPR | 2022 | [PUB] [SUPP] [PDF] [CODE] [VIDEO] |
| FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning | Universidad de Duke | CVPR | 2022 | [PUB] [SUPP] [PDF] |
| Layer-Wised Model Aggregation for Personalized Federated Learning | PolyU | CVPR | 2022 | [PUB] [SUPP] [PDF] |
| Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning | University of Central Florida | CVPR | 2022 | [PUB] [SUPP] [PDF] [CODE] |
| Federated Learning With Position-Aware Neurons | Universidad de Nankín | CVPR | 2022 | [PUB] [SUPP] [PDF] |
| RSCFed: Random Sampling Consensus Federated Semi-Supervised Learning | HKUST | CVPR | 2022 | [PUB] [SUPP] [PDF] [CODE] |
| Learn From Others and Be Yourself in Heterogeneous Federated Learning | Wuhan University | CVPR | 2022 | [PUB] [CODE] [VIDEO] |
| Robust Federated Learning With Noisy and Heterogeneous Clients | Wuhan University | CVPR | 2022 | [PUB] [SUPP] [CODE] |
| ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning | Universidad Estatal de Arizona | CVPR | 2022 | [PUB] [SUPP] [PDF] [CODE] |
| FedDC: Federated Learning With Non-IID Data via Local Drift Decoupling and Correction | National University of Defense Technology | CVPR | 2022 | [PUB] [PDF] [CODE] [解读] |
| Federated Class-Incremental Learning | CAS; Northwestern University; UTS | CVPR | 2022 | [PUB] [PDF] [CODE] |
| Fine-Tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning | PKU; JD Explore Academy; La Universidad de Sídney | CVPR | 2022 | [PUB] [PDF] |
| Differentially Private Federated Learning With Local Regularization and Sparsification | CAS | CVPR | 2022 | [PUB] [PDF] |
| Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage | University of Tennessee; Oak Ridge National Laboratory; Google Research | CVPR | 2022 | [PUB] [PDF] [CODE] [VIDEO] |
| CD2-pFed: Cyclic Distillation-Guided Channel Decoupling for Model Personalization in Federated Learning | SJTU | CVPR | 2022 | [PUB] [PDF] |
| Closing the Generalization Gap of Cross-Silo Federated Medical Image Segmentation | Univ. of Pittsburgh; Nvidia | CVPR | 2022 | [PUB] [PDF] |
| Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning | HHI | CVPR workshop | 2022 | [PUB] [PDF] [SILDES] [VIDEO] |
| MPAF: Model Poisoning Attacks to Federated Learning Based on Fake Clients | Universidad de Duke | CVPR workshop | 2022 | [PUB] [PDF] [SILDES] [VIDEO] |
| Communication-Efficient Federated Data Augmentation on Non-IID Data | UESTC | CVPR workshop | 2022 | [PUB] |
| Does Federated Dropout Actually Work? | stanford | CVPR workshop | 2022 | [PUB] [VIDEO] |
| FedIris: Towards More Accurate and Privacy-preserving Iris Recognition via Federated Template Communication | USTC; CRIPAC; CASIA | CVPR workshop | 2022 | [PUB] [SLIDES] [VIDEO] |
| Multi-Institutional Collaborations for Improving Deep Learning-Based Magnetic Resonance Image Reconstruction Using Federated Learning | Universidad Johns Hopkins | CVPR | 2021 | [PUB] [PDF] [CODE] |
| Model-Contrastive Federated Learning | NUS; Universidad de Berkeley | CVPR | 2021 | [PUB] [PDF] [CODE] [解读] |
| FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space | CUHK | CVPR | 2021 | [PUB] [PDF] [CODE] |
| Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective | Universidad de Duke | CVPR | 2021 | [PUB] [PDF] [CODE] |
| Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment | PKU | ICCV | 2021 | [PUB] |
| Ensemble Attention Distillation for Privacy-Preserving Federated Learning | University at Buffalo | ICCV | 2021 | [PUB] [PDF] |
| Collaborative Unsupervised Visual Representation Learning from Decentralized Data | NTU; SenseTime | ICCV | 2021 | [PUB] [PDF] |
| Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identification | UNT | MM | 2021 | [PUB] [PDF] |
| Federated Visual Classification with Real-World Data Distribution | MIT; Google | ECCV | 2020 | [PUB] [PDF] [VIDEO] |
| InvisibleFL: Federated Learning over Non-Informative Intermediate Updates against Multimedia Privacy Leakages | MM | 2020 | [PUB] | |
Performance Optimization of Federated Person Re-identification via Benchmark Analysis data. | UNT | MM | 2020 | [PUB] [PDF] [CODE] [解读] |
Federated Learning papers accepted by top AI and NLP conference and journal, including ACL(Annual Meeting of the Association for Computational Linguistics), NAACL(North American Chapter of the Association for Computational Linguistics), EMNLP(Conference on Empirical Methods in Natural Language Processing) and COLING(International Conference on Computational Linguistics).
| Título | Afiliación | Evento | Año | Materiales |
|---|---|---|---|---|
| A Hassle-free Algorithm for Strong Differential Privacy in Federated Learning Systems | EMNLP | 2024 | [PUB] | |
| Safely Learning with Private Data: A Federated Learning Framework for Large Language Model | EMNLP | 2024 | [PUB] | |
| FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation Models | EMNLP | 2024 | [PUB] | |
| Fisher Information-based Efficient Curriculum Federated Learning with Large Language Models | EMNLP | 2024 | [PUB] | |
| Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models | EMNLP | 2024 | [PUB] | |
| Promoting Data and Model Privacy in Federated Learning through Quantized LoRA | EMNLP Findings | 2024 | [PUB] | |
| Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models | EMNLP Findings | 2024 | [PUB] | |
| Generalizable Multilingual Hate Speech Detection on Low Resource Indian Languages using Fair Selection in Federated Learning | NAACL | 2024 | [PUB] | |
| Open-Vocabulary Federated Learning with Multimodal Prototyping | NAACL | 2024 | [PUB] | |
| Navigation as Attackers Wish? Towards Building Robust Embodied Agents under Federated Learning | NAACL | 2024 | [PUB] | |
| FedLFC: Towards Efficient Federated Multilingual Modeling with LoRA-based Language Family Clustering. | NAACL Findings | 2024 | [PUB] | |
| Personalized Federated Learning for Text Classification with Gradient-Free Prompt Tuning. | NAACL Findings | 2024 | [PUB] | |
| Can Public Large Language Models Help Private Cross-device Federated Learning? | NAACL Findings | 2024 | [PUB] | |
| Fair Federated Learning with Biased Vision-Language Models | ACL Findings | 2024 | [PUB] | |
| Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization | Universidad de Castaño | EMNLP | 2023 | [PUB] [PDF] [CODE] |
| Federated Meta-Learning for Emotion and Sentiment Aware Multi-modal Complaint Identification | IIT Patna | EMNLP | 2023 | [PUB] [CODE] |
| FedID: Federated Interactive Distillation for Large-Scale Pretraining Language Models | YNU | EMNLP | 2023 | [PUB] [CODE] |
| FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning | KAIST | EMNLP | 2023 | [PUB] [PDF] |
| Coordinated Replay Sample Selection for Continual Federated Learning | CMU | EMNLP industry Track | 2023 | [PUB] [PDF] |
| Tunable Soft Prompts are Messengers in Federated Learning | SYSU | EMNLP Findings | 2023 | [PUB] [PDF] [CODE] |
| Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms | OSU | ACL | 2023 | [PUB] [PDF] [CODE] |
| FEDLEGAL: The First Real-World Federated Learning Benchmark for Legal NLP | GOLPEAR; Peng Cheng Lab | ACL | 2023 | [PUB] [CODE] |
| Client-Customized Adaptation for Parameter-Efficient Federated Learning | ACL Findings | 2023 | [PUB] | |
| Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter | ACL Findings | 2023 | [PUB] [PDF] [CODE] | |
| Federated Domain Adaptation for Named Entity Recognition via Distilling with Heterogeneous Tag Sets | ACL Findings | 2023 | [PUB] | |
| FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre-trained Language Models | ACL Findings | 2023 | [PUB] | |
| Federated Learning of Gboard Language Models with Differential Privacy | ACL Industry Track | 2023 | [PUB] [PDF] | |
| Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling | SNU | EMNLP | 2022 | [PUB] [PDF] |
| A Federated Approach to Predicting Emojis in Hindi Tweets | Universidad de Alberta | EMNLP | 2022 | [PUB] [PDF] [CODE] |
| Federated Model Decomposition with Private Vocabulary for Text Classification | GOLPEAR; Peng Cheng Lab | EMNLP | 2022 | [PUB] [CODE] |
| Fair NLP Models with Differentially Private Text Encoders | Univ. Lille | EMNLP | 2022 | [PUB] [PDF] [CODE] |
| Federated Continual Learning for Text Classification via Selective Inter-client Transfer | DRIMCo GmbH; LMU | EMNLP Findings | 2022 | [PUB] [PDF] [CODE] |
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation kg. | Lehigh University | EMNLP Findings | 2022 | [PUB] [PDF] [CODE] |
| Dim-Krum: Backdoor-Resistant Federated Learning for NLP with Dimension-wise Krum-Based Aggregation | PKU | EMNLP Findings | 2022 | [PUB] [PDF] |
| Scaling Language Model Size in Cross-Device Federated Learning | ACL workshop | 2022 | [PUB] [PDF] | |
| Intrinsic Gradient Compression for Scalable and Efficient Federated Learning | Oxford | ACL workshop | 2022 | [PUB] [PDF] |
| ActPerFL: Active Personalized Federated Learning | Amazonas | ACL workshop | 2022 | [PUB] [PAGE] |
| FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks | USC | NAACL | 2022 | [PUB] [PDF] [CODE] |
| Federated Learning with Noisy User Feedback | USC; Amazonas | NAACL | 2022 | [PUB] [PDF] |
| Training Mixed-Domain Translation Models via Federated Learning | Amazonas | NAACL | 2022 | [PUB] [PAGE] [PDF] |
| Pretrained Models for Multilingual Federated Learning | Universidad Johns Hopkins | NAACL | 2022 | [PUB] [PDF] [CODE] |
| Federated Chinese Word Segmentation with Global Character Associations | universidad de washington | ACL workshop | 2021 | [PUB] [CODE] |
| Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation | USTC | EMNLP | 2021 | [PUB] [PDF] [CODE] [VIDEO] |
| Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories | CUHK (Shenzhen) | EMNLP | 2021 | [PUB] [CODE] [VIDEO] |
| A Secure and Efficient Federated Learning Framework for NLP | Universidad de Connecticut | EMNLP | 2021 | [PUB] [PDF] [VIDEO] |
| Distantly Supervised Relation Extraction in Federated Settings | UCAS | EMNLP workshop | 2021 | [PUB] [PDF] [CODE] |
| Federated Learning with Noisy User Feedback | USC; Amazonas | NAACL workshop | 2021 | [PUB] [PDF] |
| An Investigation towards Differentially Private Sequence Tagging in a Federated Framework | Universität Hamburg | NAACL workshop | 2021 | [PUB] |
| Understanding Unintended Memorization in Language Models Under Federated Learning | NAACL workshop | 2021 | [PUB] [PDF] | |
| FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction | CAS | EMNLP | 2020 | [PUB] [VIDEO] [解读] |
| Empirical Studies of Institutional Federated Learning For Natural Language Processing | Ping An Technology | EMNLP workshop | 2020 | [PUB] |
| Federated Learning for Spoken Language Understanding | PKU | COLING | 2020 | [PUB] |
| Two-stage Federated Phenotyping and Patient Representation Learning | Boston Children's Hospital Harvard Medical School | ACL workshop | 2019 | [PUB] [PDF] [CODE] [UC.] |
Federated Learning papers accepted by top Information Retrieval conference and journal, including SIGIR(Annual International ACM SIGIR Conference on Research and Development in Information Retrieval).
| Título | Afiliación | Evento | Año | Materiales |
|---|---|---|---|---|
| ReFer: Retrieval-Enhanced Vertical Federated Recommendation for Full Set User Benefit | JUE | SIGIR | 2024 | [PUB] |
| Revisit Targeted Model Poisoning on Federated Recommendation: Optimize via Multi-objective Transport | ZJU | SIGIR | 2024 | [PUB] |
| FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation | UQ | SIGIR | 2024 | [PUB] [PDF] [CODE] |
| FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction | Grupo Alibaba | SIGIR | 2024 | [PUB] |
| Personalized Federated Relation Classification over Heterogeneous Texts | NUDT | SIGIR | 2023 | [PUB] |
| Fine-Grained Preference-Aware Personalized Federated POI Recommendation with Data Sparsity | SDU | SIGIR | 2023 | [PUB] |
| Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures | UQ | SIGIR | 2023 | [PUB] [PDF] |
| FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical Federated Learning | Grupo Alibaba | SIGIR | 2023 | [PUB] [PDF] [CODE] |
| Edge-cloud Collaborative Learning with Federated and Centralized Features (short-paper) | ZJU | SIGIR | 2023 | [PUB] [PDF] |
| FLIRT: Federated Learning for Information Retrieval (extended-abstract) | IMT Lucca | SIGIR | 2023 | [PUB] |
| Is Non-IID Data a Threat in Federated Online Learning to Rank? | The University of Queensland | SIGIR | 2022 | [PUB] [CODE] |
| FedCT: Federated Collaborative Transfer for Recommendation | Rutgers University | SIGIR | 2021 | [PUB] [PDF] [CODE] |
| On the Privacy of Federated Pipelines | Universidad Técnica de Munich | SIGIR | 2021 | [PUB] |
| FedCMR: Federated Cross-Modal Retrieval. | Dalian University of Technology | SIGIR | 2021 | [PUB] [CODE] |
| Meta Matrix Factorization for Federated Rating Predictions. | SDU | SIGIR | 2020 | [PUB] [PDF] |
Federated Learning papers accepted by top Database conference and journal, including SIGMOD(ACM SIGMOD Conference) , ICDE(IEEE International Conference on Data Engineering) and VLDB(Very Large Data Bases Conference).
| Título | Afiliación | Evento | Año | Materiales |
|---|---|---|---|---|
| FedMix: Boosting with Data Mixture for Vertical Federated Learning | ICDE | 2024 | [PUB] | |
| FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregation | ICDE | 2024 | [PUB] | |
| Clients Help Clients: Alternating Collaboration for Semi-Supervised Federated Learning | ICDE | 2024 | [PUB] | |
| Semi-Asynchronous Online Federated Crowdsourcing | ICDE | 2024 | [PUB] | |
| AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity | ICDE | 2024 | [PUB] | |
| MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation | ICDE | 2024 | [PUB] | |
| LightTR: A Lightweight Framework for Federated Trajectory Recovery | ICDE | 2024 | [PUB] | |
| Feed: Towards Personalization-Effective Federated Learning | ICDE | 2024 | [PUB] | |
| Label Noise Correction for Federated Learning: A Secure, Efficient and Reliable Realization | ICDE | 2024 | [PUB] | |
| Fast, Robust and Interpretable Participant Contribution Estimation for Federated Learning | ICDE | 2024 | [PUB] | |
| HeteFedRec: Federated Recommender Systems with Model Heterogeneity | ICDE | 2024 | [PUB] | |
| Hide Your Model: A Parameter Transmission-free Federated Recommender System | ICDE | 2024 | [PUB] | |
| FedCTQ: A Federated-Based Framework for Accurate and Efficient Contact Tracing Query | ICDE | 2024 | [PUB] | |
| Preventing the Popular Item Embedding Based Attack in Federated Recommendations | ICDE | 2024 | [PUB] | |
| RobFL: Robust Federated Learning via Feature Center Separation and Malicious Center Detection | ICDE | 2024 | [PUB] | |
| Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly | TUM | DEEM@SIGMOD | 2024 | [PUB] |
| FedSQ: A Secure System for Federated Vector Similarity Queries | VLDB | 2024 | [PUB] | |
| FedSM: A Practical Federated Shared Mobility System | VLDB | 2024 | [PUB] | |
| OFL-W3: A One-Shot Federated Learning System on Web 3.0 | VLDB | 2024 | [PUB] | |
| Contributions Estimation in Federated Learning: A Comprehensive Experimental Evaluation | VLDB | 2024 | [PUB] | |
| OFL-W3: A One-shot Federated Learning System on Web 3.0 | VLDB | 2024 | [PUB] | |
| Uldp-FL: Federated Learning with Across Silo User-Level Differential Privacy. | VLDB | 2024 | [PUB] | |
| FedSM: A Practical Federated Shared Mobility System. | VLDB | 2024 | [PUB] | |
| FedSQ: A Secure System for Federated Vector Similarity Queries | VLDB | 2024 | [PUB] | |
| Performance-Based Pricing of Federated Learning via Auction | Grupo Alibaba | VLDB | 2024 | [PUB] [CODE] |
| A Blockchain System for Clustered Federated Learning with Peer-to-Peer Knowledge Transfer | NJU | VLDB | 2024 | [PUB] [CODE] |
| Communication Efficient and Provable Federated Unlearning | SDU; KAUST | VLDB | 2024 | [PUB] [PDF] [CODE] |
| Enhancing Decentralized Federated Learning for Non-IID Data on Heterogeneous Devices | USTC | ICDE | 2023 | [PUB] |
| Dynamic Activation of Clients and Parameters for Federated Learning over Heterogeneous Graphs | Universidad de Columbia | ICDE | 2023 | [PUB] [CODE] |
| FedKNOW: Federated Continual Learning with Signature Task Knowledge Integration at Edge | POCO | ICDE | 2023 | [PUB] [PDF] |
| Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices | SJTU | ICDE | 2023 | [PUB] [PDF] |
| Federated IoT Interaction Vulnerability Analysis | Universidad Estatal de Michigan | ICDE | 2023 | [PUB] |
| Distribution-Regularized Federated Learning on Non-IID Data | BUAA | ICDE | 2023 | [PUB] |
| Fed-SC: One-Shot Federated Subspace Clustering over High-Dimensional Data | ShanghaiTech University | ICDE | 2023 | [PUB] [CODE] |
| FLBooster: A Unified and Efficient Platform for Federated Learning Acceleration | ZJU | ICDE | 2023 | [PUB] |
| FedGTA: Topology-aware Averaging for Federated Graph Learning. | POCO | VLDB | 2023 | [PUB] [CODE] |
| FS-Real: A Real-World Cross-Device Federated Learning Platform. | Grupo Alibaba | VLDB | 2023 | [PUB] [PDF] [CODE] |
| Federated Calibration and Evaluation of Binary Classifiers. | meta | VLDB | 2023 | [PUB] [PDF] [CODE] |
| Olive: Oblivious Federated Learning on Trusted Execution Environment Against the Risk of Sparsification. | Kyoto University | VLDB | 2023 | [PUB] [PDF] [CODE] |
| Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System. | NUES | VLDB | 2023 | [PUB] [CODE] |
| Differentially Private Vertical Federated Clustering. | Purdue University | VLDB | 2023 | [PUB] [PDF] [CODE] |
| FederatedScope: A Flexible Federated Learning Platform for Heterogeneity. | Alibaba | VLDB | 2023 | [PUB] [PDF] [CODE] |
| Secure Shapley Value for Cross-Silo Federated Learning. | Kyoto University | VLDB | 2023 | [PUB] [PDF] [CODE] |
| OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization | ZJU | VLDB | 2022 | [PUB] [PDF] [CODE] |
| Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy. | NUES | VLDB | 2022 | [PUB] [CODE] |
| Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Update | PKU | VLDB | 2022 | [PUB] [PDF] [CODE] |
| FedTSC: A Secure Federated Learning System for Interpretable Time Series Classification. | GOLPEAR | VLDB | 2022 | [PUB] [CODE] |
| Improving Fairness for Data Valuation in Horizontal Federated Learning | The UBC | ICDE | 2022 | [PUB] [PDF] |
| FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneity | USTC | ICDE | 2022 | [PUB] [PDF] [CODE] |
| FedMP: Federated Learning through Adaptive Model Pruning in Heterogeneous Edge Computing. | USTC | ICDE | 2022 | [PUB] |
| Federated Learning on Non-IID Data Silos: An Experimental Study. | NUES | ICDE | 2022 | [PUB] [PDF] [CODE] |
| Enhancing Federated Learning with Intelligent Model Migration in Heterogeneous Edge Computing | USTC | ICDE | 2022 | [PUB] |
| Samba: A System for Secure Federated Multi-Armed Bandits | Univ. Clermont Auvergne | ICDE | 2022 | [PUB] [CODE] |
| FedRecAttack: Model Poisoning Attack to Federated Recommendation | ZJU | ICDE | 2022 | [PUB] [PDF] [CODE] |
| Enhancing Federated Learning with In-Cloud Unlabeled Data | USTC | ICDE | 2022 | [PUB] |
| Efficient Participant Contribution Evaluation for Horizontal and Vertical Federated Learning | USTC | ICDE | 2022 | [PUB] |
| An Introduction to Federated Computation | University of Warwick; Facebook | SIGMOD Tutorial | 2022 | [PUB] |
| BlindFL: Vertical Federated Machine Learning without Peeking into Your Data | PKU; Tencent | SIGMOD | 2022 | [PUB] [PDF] |
| An Efficient Approach for Cross-Silo Federated Learning to Rank | BUAA | ICDE | 2021 | [PUB] [RELATED PAPER(ZH)] |
| Feature Inference Attack on Model Predictions in Vertical Federated Learning | NUES | ICDE | 2021 | [PUB] [PDF] [CODE] |
| Efficient Federated-Learning Model Debugging | USTC | ICDE | 2021 | [PUB] |
| Federated Matrix Factorization with Privacy Guarantee | Purdue | VLDB | 2021 | [PUB] |
| Projected Federated Averaging with Heterogeneous Differential Privacy. | Renmin University of China | VLDB | 2021 | [PUB] [CODE] |
| Enabling SQL-based Training Data Debugging for Federated Learning | Universidad Simon Fraser | VLDB | 2021 | [PUB] [PDF] [CODE] |
| Refiner: A Reliable Incentive-Driven Federated Learning System Powered by Blockchain | ZJU | VLDB | 2021 | [PUB] |
| Tanium Reveal: A Federated Search Engine for Querying Unstructured File Data on Large Enterprise Networks | Tanium Inc. | VLDB | 2021 | [PUB] [VIDEO] |
| VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning | PKU | SIGMOD | 2021 | [PUB] |
| ExDRa: Exploratory Data Science on Federated Raw Data | SIEMENS | SIGMOD | 2021 | [PUB] |
| Joint blockchain and federated learning-based offloading in harsh edge computing environments | TJU | SIGMOD workshop | 2021 | [PUB] |
| Privacy Preserving Vertical Federated Learning for Tree-based Models | NUES | VLDB | 2020 | [PUB] [PDF] [VIDEO] [CODE] |
Federated Learning papers accepted by top Database conference and journal, including SIGCOMM(Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication), INFOCOM(IEEE Conference on Computer Communications), MobiCom(ACM/IEEE International Conference on Mobile Computing and Networking), NSDI(Symposium on Networked Systems Design and Implementation) and WWW(The Web Conference).
| Título | Afiliación | Evento | Año | Materiales |
|---|---|---|---|---|
| Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning | INFOCOM | 2024 | [PUB] | |
| Strategic Data Revocation in Federated Unlearning | INFOCOM | 2024 | [PUB] | |
| FedTC: Enabling Communication-Efficient Federated Learning via Transform Coding | INFOCOM | 2024 | [PUB] | |
| Federated Learning While Providing Model as a Service: Joint Training and Inference Optimization | INFOCOM | 2024 | [PUB] | |
| FairFed: Improving Fairness and Efficiency of Contribution Evaluation in Federated Learning via Cooperative Shapley Value | INFOCOM | 2024 | [PUB] | |
| DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service | INFOCOM | 2024 | [PUB] | |
| Tomtit: Hierarchical Federated Fine-Tuning of Giant Models based on Autonomous Synchronization | INFOCOM | 2024 | [PUB] | |
| BR-DeFedRL: Byzantine-Robust Decentralized Federated Reinforcement Learning with Fast Convergence and Communication Efficiency | INFOCOM | 2024 | [PUB] | |
| Titanic: Towards Production Federated Learning with Large Language Models | INFOCOM | 2024 | [PUB] | |
| Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression | INFOCOM | 2024 | [PUB] | |
| Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes | INFOCOM | 2024 | [PUB] | |
| Federated Analytics-Empowered Frequent Pattern Mining for Decentralized Web 3.0 Applications | INFOCOM | 2024 | [PUB] | |
| GraphProxy: Communication-Efficient Federated Graph Learning with Adaptive Proxy | INFOCOM | 2024 | [PUB] | |
| Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration | INFOCOM | 2024 | [PUB] [CODE] | |
| AeroRec: An Efficient On-Device Recommendation Framework using Federated Self-Supervised Knowledge Distillation | INFOCOM | 2024 | [PUB] | |
| Efficient and Straggler-Resistant Homomorphic Encryption for Heterogeneous Federated Learning | INFOCOM | 2024 | [PUB] | |
| Heroes: Lightweight Federated Learning with Neural Composition and Adaptive Local Update in Heterogeneous Edge Networks | INFOCOM | 2024 | [PUB] | |
| Momentum-Based Federated Reinforcement Learning with Interaction and Communication Efficiency | INFOCOM | 2024 | [PUB] | |
| Federated Offline Policy Optimization with Dual Regularization | INFOCOM | 2024 | [PUB] | |
| A Semi-Asynchronous Decentralized Federated Learning Framework via Tree-Graph Blockchain | INFOCOM | 2024 | [PUB] | |
| SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation | INFOCOM | 2024 | [PUB] | |
| Towards Efficient Asynchronous Federated Learning in Heterogeneous Edge Environments | INFOCOM | 2024 | [PUB] | |
| Federated Learning Based Integrated Sensing, Communications, and Powering Over 6G Massive-MIMO Mobile Networks | INFOCOM workshop | 2024 | [PUB] | |
| Decentralized Federated Learning Under Free-riders: Credibility Analysis | INFOCOM workshop | 2024 | [PUB] | |
| TrustBandit: Optimizing Client Selection for Robust Federated Learning Against Poisoning Attacks | INFOCOM workshop | 2024 | [PUB] | |
| Cascade: Enhancing Reinforcement Learning with Curriculum Federated Learning and Interference Avoidance — A Case Study in Adaptive Bitrate Selection | INFOCOM workshop | 2024 | [PUB] | |
| Efficient Adapting for Vision-language Foundation Model in Edge Computing Based on Personalized and Multi-Granularity Federated Learning | INFOCOM workshop | 2024 | [PUB] | |
| Distributed Link Heterogeneity Exploitation for Attention-Weighted Robust Federated Learning in 6G Networks | INFOCOM workshop | 2024 | [PUB] | |
| GAN-Based Privacy Abuse Attack on Federated Learning in IoT Networks | INFOCOM workshop | 2024 | [PUB] | |
| Fedkit: Enabling Cross-Platform Federated Learning for Android and iOS | INFOCOM workshop | 2024 | [PUB] [CODE] | |
| ASR-FED: Agnostic Straggler Resilient Federated Algorithm for Drone Networks Security | INFOCOM workshop | 2024 | [PUB] | |
| Unbiased Federated Learning for Heterogeneous Data Under Unreliable Links | INFOCOM workshop | 2024 | [PUB] | |
| Efficient Client Sampling with Compression in Heterogeneous Federated Learning | INFOCOM workshop | 2024 | [PUB] | |
| Reputation-Aware Scheduling for Secure Internet of Drones: A Federated Multi-Agent Deep Reinforcement Learning Approach | INFOCOM workshop | 2024 | [PUB] | |
| Two-Timescale Energy Optimization for Wireless Federated Learning | INFOCOM workshop | 2024 | [PUB] | |
| A Data Reconstruction Attack Against Vertical Federated Learning Based on Knowledge Transfer | INFOCOM workshop | 2024 | [PUB] | |
| Federated Learning for Energy-efficient Cooperative Perception in Connected and Autonomous Vehicles | INFOCOM workshop | 2024 | [PUB] | |
| Federated Learning-Based Cooperative Model Training for Task-Oriented Semantic Communication | INFOCOM workshop | 2024 | [PUB] | |
| FedBF16-Dynamic: Communication-Efficient Federated Learning with Adaptive Transmission | INFOCOM workshop | 2024 | [PUB] | |
| Designing Robust 6G Networks with Bimodal Distribution for Decentralized Federated Learning | INFOCOM workshop | 2024 | [PUB] | |
| Federated Distributed Deep Reinforcement Learning for Recommendation-enabled Edge Caching | INFOCOM workshop | 2024 | [PUB] | |
| Joint Optimization of Charging Time and Resource Allocation in Wireless Power Transfer Assisted Federated Learning | INFOCOM workshop | 2024 | [PUB] | |
| Joint Client Selection and Privacy Compensation for Differentially Private Federated Learning | INFOCOM workshop | 2024 | [PUB] | |
| Wireless Hierarchical Federated Aggregation Weights Design with Loss-Based-Heterogeneity | INFOCOM workshop | 2024 | [PUB] | |
| ADMarker: A Multi-Modal Federated Learning System for Monitoring Digital Biomarkers of Alzheimer's Disease | CUHK | MobiCom | 2024 | [PUB] [PDF] [CODE] |
| Accelerating the Decentralized Federated Learning via Manipulating Edges | SZU | www | 2024 | [PUB] |
| Prompt-enhanced Federated Content Representation Learning for Cross-domain Recommendation | SDNU | www | 2024 | [PUB] [PDF] [CODE] |
| PAGE: Equilibrate Personalization and Generalization in Federated Learning | XDU | www | 2024 | [PUB] [PDF] [CODE] |
| Federated Learning Vulnerabilities: Privacy Attacks with Denoising Diffusion Probabilistic Models | ECNU | www | 2024 | [PUB] |
| Co-clustering for Federated Recommender System | UIUC | www | 2024 | [PUB] |
| Incentive and Dynamic Client Selection for Federated Unlearning | BUPT | www | 2024 | [PUB] |
| Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Training | VinUniversity | www | 2024 | [PUB] [PDF] [CODE] |
| BlockDFL: A Blockchain-based Fully Decentralized Peer-to-Peer Federated Learning Framework | ZJU | www | 2024 | [PUB] [PDF] |
| Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation | UQ | www | 2024 | [PUB] [PDF] |
| FedDSE: Distribution-aware Sub-model Extraction for Federated Learning over Resource-constrained Devices | UNT | www | 2024 | [PUB] |
| Cardinality Counting in "Alcatraz": A Privacy-aware Federated Learning Approach | CSIRO's Data61 | www | 2024 | [PUB] |
| Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation | BUPT | www | 2024 | [PUB] [PDF] |
| Poisoning Federated Recommender Systems with Fake Users | USTC | www | 2024 | [PUB] [PDF] |
| Towards Energy-efficient Federated Learning via INT8-based Training on Mobile DSPs | BUPT | www | 2024 | [PUB] |
| Privacy-Preserving and Fairness-Aware Federated Learning for Critical Infrastructure Protection and Resilience | UTS | www | 2024 | [PUB] [CODE] |
| When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions | JLU | www | 2024 | [PUB] [PDF] [CODE] |
| How Few Davids Improve One Goliath: Federated Learning in Resource-Skewed Edge Computing Environments | UCSD | www | 2024 | [PUB] [CODE] [VIDEO] |
| Poisoning Attack on Federated Knowledge Graph Embedding | PolyU | www | 2024 | [PUB] [CODE] |
| FL@FM-TheWebConf'24: International Workshop on Federated Foundation Models for the Web | CUHK | WWW (Companion Volume) | 2024 | [PUB] [PAGE] |
| An Investigation into the Feasibility of Performing Federated Learning on Social Linked Data Servers | University of Southampton | WWW (Companion Volume) | 2024 | [PUB] |
| Exploring Representational Similarity Analysis to Protect Federated Learning from Data Poisoning | SYSU | WWW (Companion Volume) | 2024 | [PUB] |
| Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation | Purdue University | WWW (Companion Volume) | 2024 | [PUB] [PDF] |
| FedHLT: Efficient Federated Low-Rank Adaption with Hierarchical Language Tree for Multilingual Modeling | CUHK | WWW (Companion Volume) | 2024 | [PUB] |
| HBIAS FedAvg: Smooth Federated Learning Transition for In-use Edge Models | IIT | WWW (Companion Volume) | 2024 | [PUB] |
| Phoenix: A Federated Generative Diffusion Model | universidad | WWW (Companion Volume) | 2024 | [PUB] |
| Federated Learning in Large Model Era: Vision-Language Model for Smart City Safety Operation Management | ENN; UPC | WWW (Companion Volume) | 2024 | [PUB] |
| Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks | USTC | WWW (Companion Volume) | 2024 | [PUB] [PDF] |
| GradFilt: Class-wise Targeted Data Reconstruction from Gradients in Federated Learning | PolyU | WWW (Companion Volume) | 2024 | [PUB] |
| Detecting Poisoning Attacks on Federated Learning Using Gradient-Weighted Class Activation Mapping | ISEP | WWW (Companion Volume) | 2024 | [PUB] |
| AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving | UNT | MobiCom | 2023 | [PUB] [PDF] |
| Efficient Federated Learning for Modern NLP | Beiyou Shenzhen Institute | MobiCom | 2023 | [PDF] [解读] |
| FLASH: Towards a High-performance Hardware Acceleration Architecture for Cross-silo Federated Learning | HKUST; Clustar | NSDI | 2023 | [PUB] [SLIDE] [VIDEO] |
| To Store or Not? Online Data Selection for Federated Learning with Limited Storage. | SJTU | www | 2023 | [PUB] [PDF] |
| pFedPrompt: Learning Personalized Prompt for Vision-Language Models in Federated Learning. | PolyU | www | 2023 | [PUB] |
| Quantifying and Defending against Privacy Threats on Federated Knowledge Graph Embedding. | ZJU; HIC-ZJU | www | 2023 | [PUB] [PDF] |
| Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks | PKU | www | 2023 | [PUB] [PDF] [CODE] |
| Semi-decentralized Federated Ego Graph Learning for Recommendation | SUST | www | 2023 | [PUB] [PDF] |
| FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures. | Swinburne | www | 2023 | [PUB] [CODE] |
| FedEdge: Accelerating Edge-Assisted Federated Learning. | Swinburne | www | 2023 | [PUB] |
| Federated Node Classification over Graphs with Latent Link-type Heterogeneity. | Universidad Emory | www | 2023 | [PUB] [CODE] |
| FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection. | USTC | www | 2023 | [PUB] [PDF] [CODE] |
| Interaction-level Membership Inference Attack Against Federated Recommender Systems. | UQ | www | 2023 | [PUB] [PDF] |
| AgrEvader: Poisoning Membership Inference against Byzantine-robust Federated Learning. | Deakin University | www | 2023 | [PUB] |
| Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning. | NJU | www | 2023 | [PUB] [PDF] [CODE] |
| Federated Learning for Metaverse: A Survey. | JNU | WWW (Companion Volume) | 2023 | [PUB] [PDF] |
| Understanding the Impact of Label Skewness and Optimization on Federated Learning for Text Classification | KU Lovaina | WWW (Companion Volume) | 2023 | [PUB] |
| Privacy-Preserving Online Content Moderation: A Federated Learning Use Case. | CORTAR | WWW (Companion Volume) | 2023 | [PUB] [PDF] |
| Privacy-Preserving Online Content Moderation with Federated Learning. | CORTAR | WWW (Companion Volume) | 2023 | [PUB] |
| A Federated Learning Benchmark for Drug-Target Interaction. | University of Turin | WWW (Companion Volume) | 2023 | [PUB] [PDF] [CODE] |
| Towards a Decentralized Data Hub and Query System for Federated Dynamic Data Spaces. | TU Berlin | WWW (Companion Volume) | 2023 | [PUB] |
| 1st Workshop on Federated Learning Technologies1st Workshop on Federated Learning Technologies | University of Turin | WWW (Companion Volume) | 2023 | [PUB] |
| A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness and Privacy | CUHK | WWW (Companion Volume) | 2023 | [PUB] [PDF] |
| A Hierarchical Knowledge Transfer Framework for Heterogeneous Federated Learning | JUE | INFOCOM | 2023 | [PUB] |
| A Reinforcement Learning Approach for Minimizing Job Completion Time in Clustered Federated Learning | Universidad del Sureste | INFOCOM | 2023 | [PUB] |
| Adaptive Configuration for Heterogeneous Participants in Decentralized Federated Learning | USTC | INFOCOM | 2023 | [PUB] [PDF] |
| AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices | Guangdong University of Technology | INFOCOM | 2023 | [PUB] [PDF] |
| AOCC-FL: Federated Learning with Aligned Overlapping via Calibrated Compensation | FOTOS | INFOCOM | 2023 | [PUB] |
| Asynchronous Federated Unlearning | universidad de toronto | INFOCOM | 2023 | [PUB] [PDF] [CODE] |
| Communication-Efficient Federated Learning for Heterogeneous Edge Devices Based on Adaptive Gradient Quantization | fuente de alimentación | INFOCOM | 2023 | [PUB] [PDF] |
| Enabling Communication-Efficient Federated Learning via Distributed Compressed Sensing | Beihang University | INFOCOM | 2023 | [PUB] |
| Federated Learning under Heterogeneous and Correlated Client Availability | Inria | INFOCOM | 2023 | [PUB] [PDF] [CODE] |
| Federated Learning with Flexible Control | IBM | INFOCOM | 2023 | [PUB] [PDF] [CODE] |
| Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks | La Universidad de Sídney | INFOCOM | 2023 | [PUB] [PDF] |
| FedMoS: Taming Client Drift in Federated Learning with Double Momentum and Adaptive Selection | FOTOS | INFOCOM | 2023 | [PUB] [PDF] [CODE] |
| FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning | UNT | INFOCOM | 2023 | [PUB] [PDF] |
| Heterogeneity-Aware Federated Learning with Adaptive Client Selection and Gradient Compression | USTC | INFOCOM | 2023 | |
| Joint Edge Aggregation and Association for Cost-Efficient Multi-Cell Federated Learning | NUDT | INFOCOM | 2023 | [PUB] |
| Joint Participation Incentive and Network Pricing Design for Federated Learning | Universidad del Noroeste | INFOCOM | 2023 | [PUB] |
| More than Enough is Too Much: Adaptive Defenses against Gradient Leakage in Production Federated Learning | universidad de toronto | INFOCOM | 2023 | [PUB] [PDF] [WEIBO] |
| Network Adaptive Federated Learning: Congestion and Lossy Compression | UTAustin | INFOCOM | 2023 | [PUB] [PDF] |
| OBLIVION: Poisoning Federated Learning by Inducing Catastrophic Forgetting | The Hang Seng University of Hong Kong | INFOCOM | 2023 | [PUB] [CODE] |
| Privacy as a Resource in Differentially Private Federated Learning | BUPT | INFOCOM | 2023 | [PUB] |
| SplitGP: Achieving Both Generalization and Personalization in Federated Learning | KAIST | INFOCOM | 2023 | [PUB] [PDF] |
| SVDFed: Enabling Communication-Efficient Federated Learning via Singular-Value-Decomposition | Beihang University | INFOCOM | 2023 | [PUB] |
| Tackling System Induced Bias in Federated Learning: Stratification and Convergence Analysis | Southern University of Science and Technology | INFOCOM | 2023 | [PUB] [PDF] |
| Toward Sustainable AI: Federated Learning Demand Response in Cloud-Edge Systems via Auctions | BUPT | INFOCOM | 2023 | [PUB] [PDF] |
| Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labeling | Universidad de Castaño | INFOCOM | 2023 | [PUB] [PDF] |
| TVFL: Tunable Vertical Federated Learning towards Communication-Efficient Model Serving | USTC | INFOCOM | 2023 | [PUB] |
| PyramidFL: Fine-grained Data and System Heterogeneity-aware Client Selection for Efficient Federated Learning | Universidad Estatal de Michigan | MobiCom | 2022 | [PUB] [PDF] [CODE] |
| NestFL: efficient federated learning through progressive model pruning in heterogeneous edge computing | pmlabs | MobiCom(Poster) | 2022 | [PUB] |
| Federated learning-based air quality prediction for smart cities using BGRU model | IITM | MobiCom(Poster) | 2022 | [PUB] |
| FedHD: federated learning with hyperdimensional computing | UCSD | MobiCom(Demo) | 2022 | [PUB] [CODE] |
| Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks | Universidad de Corea | INFOCOM | 2022 | [PUB] |
| Towards Optimal Multi-Modal Federated Learning on Non-IID Data with Hierarchical Gradient Blending | universidad de toronto | INFOCOM | 2022 | [PUB] |
| Optimal Rate Adaption in Federated Learning with Compressed Communications | SZU | INFOCOM | 2022 | [PUB] [PDF] |
| The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining. | CityU | INFOCOM | 2022 | [PUB] [PDF] |
| Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling. | CUHK; AIRS ;Yale University | INFOCOM | 2022 | [PUB] [PDF] |
| Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization | Army Research Laboratory, Adelphi | INFOCOM | 2022 | [PUB] [PDF] |
| FLASH: Federated Learning for Automated Selection of High-band mmWave Sectors | NUE | INFOCOM | 2022 | [PUB] [CODE] |
| A Profit-Maximizing Model Marketplace with Differentially Private Federated Learning | CUHK; AIRES | INFOCOM | 2022 | [PUB] |
| Protect Privacy from Gradient Leakage Attack in Federated Learning | PolyU | INFOCOM | 2022 | [PUB] [SLIDE] |
| FedFPM: A Unified Federated Analytics Framework for Collaborative Frequent Pattern Mining. | SJTU | INFOCOM | 2022 | [PUB] [CODE] |
| An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning | SWJTU;THU | www | 2022 | [PUB] [PDF] [CODE] |
| LocFedMix-SL: Localize, Federate, and Mix for Improved Scalability, Convergence, and Latency in Split Learning | Universidad Yonsei | www | 2022 | [PUB] |
| Federated Unlearning via Class-Discriminative Pruning | PolyU | www | 2022 | [PUB] [PDF] [CODE] |
| FedKC: Federated Knowledge Composition for Multilingual Natural Language Understanding | Purdue | www | 2022 | [PUB] |
| Powering Multi-Task Federated Learning with Competitive GPU Resource Sharing. | WWW (Companion Volume) | 2022 | ||
| Federated Bandit: A Gossiping Approach | Universidad de California | SIGMETRICS | 2021 | [PUB] [PDF] |
| Hermes: an efficient federated learning framework for heterogeneous mobile clients | Universidad de Duke | MobiCom | 2021 | [PUB] |
| Federated mobile sensing for activity recognition | Samsung AI Center | MobiCom | 2021 | [PUB] [PAGE] [TALKS] [VIDEO] |
| Learning for Learning: Predictive Online Control of Federated Learning with Edge Provisioning. | Universidad de Nankín | INFOCOM | 2021 | [PUB] |
| Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation. | Purdue | INFOCOM | 2021 | [PUB] [PDF] |
| FAIR: Quality-Aware Federated Learning with Precise User Incentive and Model Aggregation | JUE | INFOCOM | 2021 | [PUB] |
| Sample-level Data Selection for Federated Learning | USTC | INFOCOM | 2021 | [PUB] |
| To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices | Xidian University; CAS | INFOCOM | 2021 | [PUB] [PDF] |
| Cost-Effective Federated Learning Design | CUHK; AIRS; Universidad de Yale | INFOCOM | 2021 | [PUB] [PDF] |
| An Incentive Mechanism for Cross-Silo Federated Learning: A Public Goods Perspective | The UBC | INFOCOM | 2021 | [PUB] |
| Resource-Efficient Federated Learning with Hierarchical Aggregation in Edge Computing | USTC | INFOCOM | 2021 | [PUB] |
| FedServing: A Federated Prediction Serving Framework Based on Incentive Mechanism. | Jinan University; CityU | INFOCOM | 2021 | [PUB] [PDF] |
| Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach | Universidad Estatal de Arizona | INFOCOM | 2021 | [PUB] [PDF] |
| Dual Attention-Based Federated Learning for Wireless Traffic Prediction | King Abdullah University of Science and Technology | INFOCOM | 2021 | [PUB] [PDF] [CODE] |
| FedSens: A Federated Learning Approach for Smart Health Sensing with Class Imbalance in Resource Constrained Edge Computing | University of Notre Dame | INFOCOM | 2021 | [PUB] |
| P-FedAvg: Parallelizing Federated Learning with Theoretical Guarantees | SYSU; Guangdong Key Laboratory of Big Data Analysis and Processing | INFOCOM | 2021 | [PUB] |
| Meta-HAR: Federated Representation Learning for Human Activity Recognition. | Universidad de Alberta | www | 2021 | [PUB] [PDF] [CODE] |
| PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization | PKU | www | 2021 | [PUB] [PDF] [CODE] |
| Communication Efficient Federated Generalized Tensor Factorization for Collaborative Health Data Analytics | emory | www | 2021 | [PUB] [CODE] |
| Hierarchical Personalized Federated Learning for User Modeling | USTC | www | 2021 | [PUB] |
| Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data | PKU | www | 2021 | [PUB] [PDF] [SLIDE] [CODE] |
| Incentive Mechanism for Horizontal Federated Learning Based on Reputation and Reverse Auction | SYSU | www | 2021 | [PUB] |
| Physical-Layer Arithmetic for Federated Learning in Uplink MU-MIMO Enabled Wireless Networks. | Universidad de Nankín | INFOCOM | 2020 | [PUB] |
| Optimizing Federated Learning on Non-IID Data with Reinforcement Learning | universidad de toronto | INFOCOM | 2020 | [PUB] [SLIDE] [CODE] [解读] |
| Enabling Execution Assurance of Federated Learning at Untrusted Participants | JUE | INFOCOM | 2020 | [PUB] [CODE] |
| Billion-scale federated learning on mobile clients: a submodel design with tunable privacy | SJTU | MobiCom | 2020 | [PUB] |
| Federated Learning over Wireless Networks: Optimization Model Design and Analysis | La Universidad de Sídney | INFOCOM | 2019 | [PUB] [CODE] |
| Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning | Wuhan University | INFOCOM | 2019 | [PUB] [PDF] [UC.] |
| InPrivate Digging: Enabling Tree-based Distributed Data Mining with Differential Privacy | Collaborative Innovation Center of Geospatial Technology | INFOCOM | 2018 | [PUB] |
Federated Learning papers accepted by top Database conference and journal, including OSDI(USENIX Symposium on Operating Systems Design and Implementation), SOSP(Symposium on Operating Systems Principles), ISCA(International Symposium on Computer Architecture), MLSys(Conference on Machine Learning and Systems), EuroSys(European Conference on Computer Systems), TPDS(IEEE Transactions on Parallel and Distributed Systems), DAC(Design Automation Conference), TOCS(ACM Transactions on Computer Systems), TOS(ACM Transactions on Storage), TCAD(IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems), TC(IEEE Transactions on Computers).
| Título | Afiliación | Evento | Año | Materiales |
|---|---|---|---|---|
| AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems. | CAD | 2024 | [PUB] | |
| Fake Node-Based Perception Poisoning Attacks against Federated Object Detection Learning in Mobile Computing Networks | CAD | 2024 | [PUB] | |
| Flagger: Cooperative Acceleration for Large-Scale Cross-Silo Federated Learning Aggregation | ISCA | 2024 | [PUB] | |
| FedTrans: Efficient Federated Learning via Multi-Model Transformation | UIUC | MLSys | 2024 | [PUB] [PDF] |
| LIFL: A Lightweight, Event-driven Serverless Platform for Federated Learning | UC Riverside | MLSys | 2024 | [PUB] [PDF] |
| HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning | Universidad de Corea | MLSys | 2024 | [PUB] [PDF] [CODE] |
| DeTA: Minimizing Data Leaks in Federated Learning via Decentralized and Trustworthy Aggregation | IBM Research | EuroSys | 2024 | [PUB] |
| FLOAT: Federated Learning Optimizations with Automated Tuning | Tecnología de Virginia | EuroSys | 2024 | [PUB] [CODE] |
| Totoro: A Scalable Federated Learning Engine for the Edge | UCSC | EuroSys | 2024 | [PUB] |
| Dordis: Efficient Federated Learning with Dropout-Resilient Differential Privacy | HKUST | EuroSys | 2024 | [PUB] [PDF] [CODE] |
| FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN | EuroSys workshop | 2024 | [PUB] | |
| ALS Algorithm for Robust and Communication-Efficient Federated Learning | EuroSys workshop | 2024 | [PUB] | |
| FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission. | EuroSys workshop | 2024 | [PUB] | |
| Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting. | TPDS | 2024 | [PUB] | |
| SR-FDIL: Synergistic Replay for Federated Domain-Incremental Learning | TPDS | 2024 | [PUB] | |
| FedVeca: Federated Vectorized Averaging on Non-IID Data With Adaptive Bi-Directional Global Objective | TPDS | 2024 | [PUB] | |
| Trusted Model Aggregation With Zero-Knowledge Proofs in Federated Learning. | TPDS | 2024 | [PUB] | |
| Accelerating Communication-Efficient Federated Multi-Task Learning With Personalization and Fairness. | TPDS | 2024 | [PUB] | |
| Privacy-Preserving Data Selection for Horizontal and Vertical Federated Learning. | TPDS | 2024 | [PUB] | |
| High-Performance Hardware Acceleration Architecture for Cross-Silo Federated Learning | TPDS | 2024 | [PUB] | |
| Joint Participant and Learning Topology Selection for Federated Learning in Edge Clouds | TPDS | 2024 | [PUB] | |
| Synchronize Only the Immature Parameters: Communication-Efficient Federated Learning By Freezing Parameters Adaptively | SJTU | TPDS | 2024 | [PUB] |
| FedREM: Guided Federated Learning in the Presence of Dynamic Device Unpredictability | SYSU | TPDS | 2024 | [PUB] |
| Fed-RAC: Resource-Aware Clustering for Tackling Heterogeneity of Participants in Federated Learning | IITP | TPDS | 2024 | [PUB] [PDF] |
| Taking Advantage of the Mistakes: Rethinking Clustered Federated Learning for IoT Anomaly Detection | UVIC | TPDS | 2024 | [PUB] |
| FedICT: Federated Multi-Task Distillation for Multi-Access Edge Computing | UCAS | TPDS | 2024 | [PUB] [PDF] |
| Collaboration in Federated Learning With Differential Privacy: A Stackelberg Game Analysis | SYSU | TPDS | 2024 | [PUB] |
| FAST: Enhancing Federated Learning Through Adaptive Data Sampling and Local Training | USTC | TPDS | 2024 | [PUB] |
| EcoFed: Efficient Communication for DNN Partitioning-Based Federated Learning | University of St Andrews | TPDS | 2024 | [PUB] [PDF] [CODE] |
| FedHAP: Federated Hashing With Global Prototypes for Cross-Silo Retrieval | JUE | TPDS | 2024 | [PUB] [PDF] |
| FlexFL: Heterogeneous Federated Learning via APoZ-Guided Flexible Pruning in Uncertain Scenarios. | TCAD | 2024 | [PUB] | |
| Personalized Meta-Federated Learning for IoT-Enabled Health Monitoring | TCAD | 2024 | [PUB] | |
| NebulaFL: Self-Organizing Efficient Multilayer Federated Learning Framework With Adaptive Load Tuning in Heterogeneous Edge Systems | TCAD | 2024 | [PUB] | |
| CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance | TCAD | 2024 | [PUB] | |
| FedStar: Efficient Federated Learning on Heterogeneous Communication Networks | USTC | TCAD | 2024 | [PUB] |
| Lithography Hotspot Detection Based on Heterogeneous Federated Learning With Local Adaptation and Feature Selection | ZJU | TCAD | 2024 | [PUB] [PDF] |
| FedComp: A Federated Learning Compression Framework for Resource-Constrained Edge Computing Devices | GOLPEAR | TCAD | 2024 | [PUB] |
| BSR-FL: An Efficient Byzantine-Robust Privacy-Preserving Federated Learning Framework | TC | 2024 | [PUB] | |
| User-Distribution-Aware Federated Learning for Efficient Communication and Fast Inference | ECNU; shu | TC | 2024 | [PUB] |
| FedRFQ: Prototype-Based Federated Learning With Reduced Redundancy, Minimal Failure, and Enhanced Quality | SDU | TC | 2024 | [PUB] [PDF] |
| Value of Information: A Comprehensive Metric for Client Selection in Federated Edge Learning | SDU | TC | 2024 | [PUB] |
| Age-Aware Data Selection and Aggregator Placement for Timely Federated Continual Learning in Mobile Edge Computing | DLUT | TC | 2024 | [PUB] |
| FedGKD: Toward Heterogeneous Federated Learning via Global Knowledge Distillation | FOTOS | TC | 2024 | [PUB] [PDF] |
| Digital Twin-Assisted Federated Learning Service Provisioning Over Mobile Edge Networks | SDU | TC | 2024 | [PUB] |
| REFL: Resource-Efficient Federated Learning | QMUL | EuroSys | 2023 | [PUB] [PDF] [CODE] |
| A First Look at the Impact of Distillation Hyper-Parameters in Federated Knowledge Distillation | EuroSys workshop | 2023 | [PUB] | |
| Towards Practical Few-shot Federated NLP | EuroSys workshop | 2023 | [PUB] | |
| Can Fair Federated Learning Reduce the need for Personalisation? | EuroSys workshop | 2023 | [PUB] | |
| Gradient-less Federated Gradient Boosting Tree with Learnable Learning Rates | EuroSys workshop | 2023 | [PUB] | |
| Towards Robust and Bias-free Federated Learning | EuroSys workshop | 2023 | [PUB] | |
| FedTree: A Federated Learning System For Trees | Universidad de Berkeley | MLSys | 2023 | [PUB] [CODE] |
| FLINT: A Platform for Federated Learning Integration | MLSys | 2023 | [PUB] [PDF] | |
| On Noisy Evaluation in Federated Hyperparameter Tuning | CMU | MLSys | 2023 | [PUB] [PDF] [CODE] |
| GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning | UBC | MLSys | 2023 | [PUB] [PDF] [CODE] |
| Self-Supervised On-Device Federated Learning From Unlabeled Streams. | FDU | TCAD | 2023 | [PUB] [PDF] |
| Optimizing Training Efficiency and Cost of Hierarchical Federated Learning in Heterogeneous Mobile-Edge Cloud Computing | ECNU | TCAD | 2023 | [PUB] |
| Lightweight Blockchain-Empowered Secure and Efficient Federated Edge Learning | University of Exeter | TC | 2023 | [PUB] |
| Towards Data-Independent Knowledge Transfer in Model-Heterogeneous Federated Learning | PolyU | TC | 2023 | [PUB] |
| A New Federated Scheduling Algorithm for Arbitrary-Deadline DAG Tasks | NEFU | TC | 2023 | [PUB] |
| Privacy-Enhanced Decentralized Federated Learning at Dynamic Edge | SDU | TC | 2023 | [PUB] |
| Byzantine-Resilient Federated Learning at Edge | SDU | TC | 2023 | [PUB] [PDF] |
| PrivAim: A Dual-Privacy Preserving and Quality-Aware Incentive Mechanism for Federated Learning | CSU | TC | 2023 | [PUB] |
| Accelerating Federated Learning With a Global Biased Optimiser | University of Exeter | TC | 2023 | [PUB] [PDF] [CODE] |
| Type-Aware Federated Scheduling for Typed DAG Tasks on Heterogeneous Multicore Platforms | TU Dortmund University | TC | 2023 | [PUB] [CODE] |
| Sandbox Computing: A Data Privacy Trusted Sharing Paradigm Via Blockchain and Federated Learning. | BUPT | TC | 2023 | [PUB] |
| CHEESE: Distributed Clustering-Based Hybrid Federated Split Learning Over Edge Networks | SUDA | TPDS | 2023 | [PUB] |
| Hierarchical Federated Learning With Momentum Acceleration in Multi-Tier Networks | University of Sydney | TPDS | 2023 | [PUB] [PDF] |
| Dap-FL: Federated Learning Flourishes by Adaptive Tuning and Secure Aggregation | Xidian University | TPDS | 2023 | [PUB] [PDF] [CODE] |
| Collaborative Intrusion Detection System for SDVN: A Fairness Federated Deep Learning Approach | Universidad de Anhui | TPDS | 2023 | [PUB] |
| Energy-Aware, Device-to-Device Assisted Federated Learning in Edge Computing | ANU | TPDS | 2023 | [PUB] |
| Faster Federated Learning With Decaying Number of Local SGD Steps | University of Exeter | TPDS | 2023 | [PUB] [PDF] [CODE] |
| DRFL: Federated Learning in Diabetic Retinopathy Grading Using Fundus Images | National Institute of Technology Silchar | TPDS | 2023 | [PUB] |
| FedProf: Selective Federated Learning Based on Distributional Representation Profiling | Peng Cheng Laboratory | TPDS | 2023 | [PUB] [PDF] [UC] |
| Federated Ensemble Model-Based Reinforcement Learning in Edge Computing | University of Exeter | TPDS | 2023 | [PUB] [PDF] |
| Incentive Mechanism Design for Joint Resource Allocation in Blockchain-Based Federated Learning. | IUPUI | TPDS | 2023 | [PUB] [PDF] |
| HiFlash: Communication-Efficient Hierarchical Federated Learning With Adaptive Staleness Control and Heterogeneity-Aware Client-Edge Association. | SYSU | TPDS | 2023 | [PUB] [PDF] |
| From Deterioration to Acceleration: A Calibration Approach to Rehabilitating Step Asynchronism in Federated Optimization. | PolyU | TPDS | 2023 | [PUB] [PDF] [CODE] |
| Federated Learning Over Coupled Graphs | XJTU | TPDS | 2023 | [PUB] [PDF] |
| Privacy vs. Efficiency: Achieving Both Through Adaptive Hierarchical Federated Learning | NUDT | TPDS | 2023 | [PUB] |
| On Model Transmission Strategies in Federated Learning With Lossy Communications | SZU | TPDS | 2023 | [PUB] |
| Scheduling Algorithms for Federated Learning With Minimal Energy Consumption | University of Bordeaux | TPDS | 2023 | [PUB] [PDF] [CODE] |
| Auction-Based Cluster Federated Learning in Mobile Edge Computing Systems | GOLPEAR | TPDS | 2023 | [PUB] [PDF] |
| Personalized Edge Intelligence via Federated Self-Knowledge Distillation. | FOTOS | TPDS | 2023 | [PUB] [CODE] |
| Design of a Quantization-Based DNN Delta Compression Framework for Model Snapshots and Federated Learning. | GOLPEAR | TPDS | 2023 | [PUB] |
| Multi-Job Intelligent Scheduling With Cross-Device Federated Learning. | Baidu | TPDS | 2023 | [PUB] [PDF] |
| Data-Centric Client Selection for Federated Learning Over Distributed Edge Networks. | IIT | TPDS | 2023 | [PUB] |
| GossipFL: A Decentralized Federated Learning Framework With Sparsified and Adaptive Communication. | HKBU | TPDS | 2023 | [PUB] |
| FedMDS: An Efficient Model Discrepancy-Aware Semi-Asynchronous Clustered Federated Learning Framework. | CQU | TPDS | 2023 | [PUB] |
| HierFedML: Aggregator Placement and UE Assignment for Hierarchical Federated Learning in Mobile Edge Computing. | DUT | TPDS | 2023 | [PUB] |
| Data selection for efficient model update in federated learning | EuroSys workshop | 2022 | [PUB] | |
| Empirical analysis of federated learning in heterogeneous environments | EuroSys workshop | 2022 | [PUB] | |
| BAFL: A Blockchain-Based Asynchronous Federated Learning Framework | TC | 2022 | [PUB] [CODE] | |
| L4L: Experience-Driven Computational Resource Control in Federated Learning | TC | 2022 | [PUB] | |
| Adaptive Federated Learning on Non-IID Data With Resource Constraint | TC | 2022 | [PUB] | |
| Locking Protocols for Parallel Real-Time Tasks With Semaphores Under Federated Scheduling. | TCAD | 2022 | [PUB] | |
| Client Scheduling and Resource Management for Efficient Training in Heterogeneous IoT-Edge Federated Learning | ECNU | TCAD | 2022 | [PUB] |
| PervasiveFL: Pervasive Federated Learning for Heterogeneous IoT Systems. | ECNU | TCAD | 2022 | [PUB] |
| FHDnn: communication efficient and robust federated learning for AIoT networks | UC San Diego | CAD | 2022 | [PUB] |
| A Decentralized Federated Learning Framework via Committee Mechanism With Convergence Guarantee | SYSU | TPDS | 2022 | [PUB] [PDF] |
| Improving Federated Learning With Quality-Aware User Incentive and Auto-Weighted Model Aggregation | JUE | TPDS | 2022 | [PUB] |
| $f$funcX: Federated Function as a Service for Science. | SUST | TPDS | 2022 | [PUB] [PDF] |
| Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation | NUST | TPDS | 2022 | [PUB] [PDF] [CODE] |
| Adaptive Federated Deep Reinforcement Learning for Proactive Content Caching in Edge Computing. | CQU | TPDS | 2022 | [PUB] |
| TDFL: Truth Discovery Based Byzantine Robust Federated Learning | POCO | TPDS | 2022 | [PUB] |
| Federated Learning With Nesterov Accelerated Gradient | La Universidad de Sídney | TPDS | 2022 | [PUB] [PDF] |
| FedGraph: Federated Graph Learning with Intelligent Sampling | UoA | TPDS | 2022 | [PUB] [CODE] [解读] |
| AUCTION: Automated and Quality-Aware Client Selection Framework for Efficient Federated Learning. | JUE | TPDS | 2022 | [PUB] |
| DONE: Distributed Approximate Newton-type Method for Federated Edge Learning. | University of Sydney | TPDS | 2022 | [PUB] [PDF] [CODE] |
| Flexible Clustered Federated Learning for Client-Level Data Distribution Shift. | CQU | TPDS | 2022 | [PUB] [PDF] [CODE] |
| Min-Max Cost Optimization for Efficient Hierarchical Federated Learning in Wireless Edge Networks. | Xidian University | TPDS | 2022 | [PUB] |
| LightFed: An Efficient and Secure Federated Edge Learning System on Model Splitting. | CSU | TPDS | 2022 | [PUB] |
| On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Federated Learning. | Purdue | TPDS | 2022 | [PUB] [PDF] [CODE] |
| Incentive-Aware Autonomous Client Participation in Federated Learning. | Sun Yat-sen University | TPDS | 2022 | [PUB] |
| Communicational and Computational Efficient Federated Domain Adaptation. | HKUST | TPDS | 2022 | [PUB] |
| Decentralized Edge Intelligence: A Dynamic Resource Allocation Framework for Hierarchical Federated Learning. | UNT | TPDS | 2022 | [PUB] |
| Differentially Private Byzantine-Robust Federated Learning. | Qufu Normal University | TPDS | 2022 | [PUB] |
| Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing. | University of Exeter | TPDS | 2022 | [PUB] [PDF] [CODE] |
| Reputation-Aware Hedonic Coalition Formation for Efficient Serverless Hierarchical Federated Learning. | BUAA | TPDS | 2022 | [PUB] |
| Differentially Private Federated Temporal Difference Learning. | Stony Brook University | TPDS | 2022 | [PUB] |
| Towards Efficient and Stable K-Asynchronous Federated Learning With Unbounded Stale Gradients on Non-IID Data. | XJTU | TPDS | 2022 | [PUB] [PDF] |
| Communication-Efficient Federated Learning With Compensated Overlap-FedAvg. | UCE | TPDS | 2022 | [PUB] [PDF] [CODE] |
| PAPAYA: Practical, Private, and Scalable Federated Learning. | Meta IA | MLSys | 2022 | [PUB] [PDF] |
| LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning | USC | MLSys | 2022 | [PUB] [PDF] [CODE] |
| Accelerated Training via Device Similarity in Federated Learning | EuroSys workshop | 2021 | [PUB] | |
| Towards Federated Learning with Attention Transfer to Mitigate System and Data Heterogeneity of Clients | EuroSys workshop | 2021 | [PUB] | |
| Towards Mitigating Device Heterogeneity in Federated Learning via Adaptive Model Quantization | EuroSys workshop | 2021 | [PUB] | |
| SAFA: A Semi-Asynchronous Protocol for Fast Federated Learning With Low Overhead | Universidad de Warwick | TC | 2021 | [PDF] [PUB] [CODE] |
| Efficient Federated Learning for Cloud-Based AIoT Applications | ECNU | TCAD | 2021 | [PUB] |
| HADFL: Heterogeneity-aware Decentralized Federated Learning Framework | USTC | CAD | 2021 | [PDF] [PUB] |
| Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration. | GMU | CAD | 2021 | [PDF] [PUB] |
| FedLight: Federated Reinforcement Learning for Autonomous Multi-Intersection Traffic Signal Control. | ECNU | CAD | 2021 | [PUB] |
| Oort: Efficient Federated Learning via Guided Participant Selection | Universidad de Michigan | OSDI | 2021 | [PUB] [PDF] [CODE] [SLIDES] [VIDEO] |
| Towards Efficient Scheduling of Federated Mobile Devices Under Computational and Statistical Heterogeneity. | Old Dominion University | TPDS | 2021 | [PUB] [PDF] |
| Self-Balancing Federated Learning With Global Imbalanced Data in Mobile Systems. | CQU | TPDS | 2021 | [PUB] [CODE] |
| An Efficiency-Boosting Client Selection Scheme for Federated Learning With Fairness Guarantee | RABO | TPDS | 2021 | [PUB] [PDF] [解读] |
| Proof of Federated Learning: A Novel Energy-Recycling Consensus Algorithm. | Beijing Normal University | TPDS | 2021 | [PUB] [PDF] |
| Biscotti: A Blockchain System for Private and Secure Federated Learning. | UBC | TPDS | 2021 | [PUB] |
| Mutual Information Driven Federated Learning. | Deakin University | TPDS | 2021 | [PUB] |
| Accelerating Federated Learning Over Reliability-Agnostic Clients in Mobile Edge Computing Systems. | Universidad de Warwick | TPDS | 2021 | [PUB] [PDF] |
| FedSCR: Structure-Based Communication Reduction for Federated Learning. | HKU | TPDS | 2021 | [PUB] |
| FedScale: Benchmarking Model and System Performance of Federated Learning | Universidad de Michigan | SOSP workshop / ICML 2022 | 2021 | [PUB] [PDF] [CODE] [解读] |
| Redundancy in cost functions for Byzantine fault-tolerant federated learning | SOSP workshop | 2021 | [PUB] | |
| Towards an Efficient System for Differentially-private, Cross-device Federated Learning | SOSP workshop | 2021 | [PUB] | |
| GradSec: a TEE-based Scheme Against Federated Learning Inference Attacks | SOSP workshop | 2021 | [PUB] | |
| Community-Structured Decentralized Learning for Resilient EI. | SOSP workshop | 2021 | [PUB] | |
| Separation of Powers in Federated Learning (Poster Paper) | IBM Research | SOSP workshop | 2021 | [PUB] [PDF] |
| Towards federated unsupervised representation learning | EuroSys workshop | 2020 | [PUB] | |
| CoLearn: enabling federated learning in MUD-compliant IoT edge networks | EuroSys workshop | 2020 | [PUB] | |
| LDP-Fed: federated learning with local differential privacy. | EuroSys workshop | 2020 | [PUB] | |
| Accelerating Federated Learning via Momentum Gradient Descent. | USTC | TPDS | 2020 | [PUB] [PDF] |
| Towards Fair and Privacy-Preserving Federated Deep Models. | NUES | TPDS | 2020 | [PUB] [PDF] [CODE] |
| Federated Optimization in Heterogeneous Networks | CMU | MLSys | 2020 | [PUB] [PDF] [CODE] |
| Towards Federated Learning at Scale: System Design | MLSys | 2019 | [PUB] [PDF] [解读] |
Federated Learning papers accepted by top conference and journal in the other fields, including ICSE(International Conference on Software Engineering), FOCS(IEEE Annual Symposium on Foundations of Computer Science), STOC(Symposium on the Theory of Computing).
| Título | Afiliación | Evento | Año | Materiales |
|---|---|---|---|---|
| F-CodeLLM: A Federated Learning Framework for Adapting Large Language Models to Practical Software Development | SYSU | ICSE Companion | 2024 | PUB |
| Raft Protocol for Fault Tolerance and Self-Recovery in Federated Learning | SINTEF Digital | SEAMS@ICSE | 2024 | PUB |
| FedDebug: Systematic Debugging for Federated Learning Applications. | Tecnología de Virginia | ICSE | 2023 | pub pdf code |
| FedSlice: Protecting Federated Learning Models from Malicious Participants with Model Slicing. | PKU | ICSE | 2023 | pub code |
| Towards a Self-Adaptive Architecture for Federated Learning of Industrial Automation Systems | SEAMS@ICSE workshop | 2021 | pub | |
| Federated Machine Learning as a Self-Adaptive Problem | SEAMS@ICSE workshop | 2021 | pub |
This section partially refers to DBLP search engine and repositories Awesome-Federated-Learning-on-Graph-and-GNN-papers and Awesome-Federated-Machine-Learning.
| Título | Afiliación | Evento | Año | Materiales |
|---|---|---|---|---|
| FedGCN: Convergence and Communication Tradeoffs in Federated Training of Graph Convolutional Networks | CMU | NeurIPS ? | 2023 | [PDF] [CODE] |
| Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking | CMU | NeurIPS Dataset Track ? | 2023 | [PDF] [DATASET] [CODE] |
| Federated Visualization: A Privacy-Preserving Strategy for Aggregated Visual Query. | ZJU | IEEE Trans. Vis. Computadora. Gráfico. ? | 2023 | [PUB] [PDF] |
| Personalized Subgraph Federated Learning | KAIST | ICML ? | 2023 | [PDF] |
| Semi-decentralized Federated Ego Graph Learning for Recommendation | SUST | WWW:mortar_board: | 2023 | [PUB] [PDF] |
| Federated Graph Neural Network for Fast Anomaly Detection in Controller Area Networks | ECUST | IEEE Trans. inf. Forensics Secur. ? | 2023 | [PUB] |
| Federated Learning Over Coupled Graphs | XJTU | IEEE Trans. Parallel Distributed Syst. ? | 2023 | [PUB] [PDF] |
| HetVis: A Visual Analysis Approach for Identifying Data Heterogeneity in Horizontal Federated Learning | Nankai University | IEEE Trans. Vis. Computadora. Gráfico. ? | 2023 | [PUB] [PDF] |
| Federated Learning on Non-IID Graphs via Structural Knowledge Sharing | UTS | AAAI ? | 2023 | [PDF] [CODE] |
| FedGS: Federated Graph-based Sampling with Arbitrary Client Availability | XMU | AAAI ? | 2023 | [PDF] [CODE] |
| An Information Theoretic Perspective for Heterogeneous Subgraph Federated Learning. | PKU | DASFAA | 2023 | [PUB] |
| GraphCS: Graph-based client selection for heterogeneity in federated learning | NUDT | J. Parallel Distributed Comput. | 2023 | [PUB] |
| Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain Approach | BUPT | IEEE Trans. Neural Networks Learn. Syst. | 2023 | [PUB] [PDF] |
| Short-Term Traffic Flow Prediction Based on Graph Convolutional Networks and Federated Learning | ZUEL | IEEE Trans. Intell. Transp. Syst. | 2023 | [PUB] |
| Hyper-Graph Attention Based Federated Learning Methods for Use in Mental Health Detection. | HVL | IEEE J. Biomed. Informática de la Salud | 2023 | [PUB] |
| Federated Learning-Based Cross-Enterprise Recommendation With Graph Neural | IEEE Trans. Ind. Informatics | 2023 | [PUB] | |
| Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning | ZJUT | IEEE Trans. Computadora. Soc. Syst. | 2023 | [PUB] [PDF] [CODE] |
| ESA-FedGNN: Efficient secure aggregation for federated graph neural networks. | Peer Peer Netw. Aplica. | 2023 | [PUB] | |
| FedCKE: Cross-Domain Knowledge Graph Embedding in Federated Learning | SWJTU | IEEE Trans. Grandes datos | 2023 | [PUB] |
| Asynchronous federated learning with directed acyclic graph-based blockchain in edge computing: Overview, design, and challenges. | Expert Syst. Aplica. | 2023 | [PUB] | |
| FedGR: Federated Graph Neural Network for Recommendation System | COPA | Axiomas | 2023 | [PUB] |
| S-Glint: Secure Federated Graph Learning With Traffic Throttling and Flow Scheduling. | IEEE Trans. Green Commun. Netw. | 2023 | [PUB] | |
| FedAGCN: A traffic flow prediction framework based on federated learning and Asynchronous Graph Convolutional Network | Aplica. Soft Comput. | 2023 | [PUB] | |
| GDFed: Dynamic Federated Learning for Heterogenous Device Using Graph Neural Network | KHU | ICOIN | 2023 | [PUB] [CODE] |
| Coordinated Scheduling and Decentralized Federated Learning Using Conflict Clustering Graphs in Fog-Assisted IoD Networks | UBC | IEEE Trans. Veh. Tecnología. | 2023 | [PUB] |
| FedRule: Federated Rule Recommendation System with Graph Neural Networks | CMU | IoTDI | 2023 | [PUB] [PDF] [CODE] |
| FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy | SJTU | KDD ? | 2022 | [PUB] [PDF] |
| FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Platform for Federated Graph Learning | Alibaba | KDD (Best Paper Award) ? | 2022 | [PDF] [CODE] [PUB] |
| Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning | SJTU | ICML ? | 2022 | [PUB] [CODE] |
Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting kg. | ZJU | IJCAI ? | 2022 | [PUB] [PDF] [CODE] |
| Personalized Federated Learning With a Graph | UTS | IJCAI ? | 2022 | [PUB] [PDF] [CODE] |
| Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification | ZJU | IJCAI ? | 2022 | [PUB] [PDF] |
| SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data | USC | AAAI:mortar_board: | 2022 | [PUB] [PDF] [CODE] [解读] |
| FedGraph: Federated Graph Learning with Intelligent Sampling | UoA | TPDS ? | 2022 | [PUB] [CODE] [解读] |
Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications surv. | Universidad de Virginia | SIGKDD Explor. | 2022 | [PUB] [PDF] |
| Semantic Vectorization: Text- and Graph-Based Models. | IBM Research | Federated Learning | 2022 | [PUB] |
| GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs | IIT | ICDM | 2022 | [PUB] [PDF] [解读] |
| More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks | TU Delft | ACSAC | 2022 | [PUB] [PDF] |
| FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction | UESTC | TMI | 2022 | [PUB] [PDF] |
| SemiGraphFL: Semi-supervised Graph Federated Learning for Graph Classification. | PKU | PPSN | 2022 | [PUB] |
| Federated Spatio-Temporal Traffic Flow Prediction Based on Graph Convolutional Network | TJU | WCSP | 2022 | [PUB] |
| A federated graph neural network framework for privacy-preserving personalization | JUE | Comunicaciones de la naturaleza | 2022 | [PUB] [CODE] [解读] |
| Malicious Transaction Identification in Digital Currency via Federated Graph Deep Learning | POCO | INFOCOM Workshops | 2022 | [PUB] |
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation kg. | Lehigh University | EMNLP | 2022 | [PUB] [PDF] [CODE] |
| Power Allocation for Wireless Federated Learning using Graph Neural Networks | Rice University | ICASSP | 2022 | [PUB] [PDF] [CODE] |
| Privacy-Preserving Federated Multi-Task Linear Regression: A One-Shot Linear Mixing Approach Inspired By Graph Regularization | UC | ICASSP | 2022 | [PUB] [PDF] [CODE] |
| Graph-regularized federated learning with shareable side information | NWPU | Knowl. Based Syst. | 2022 | [PUB] |
Federated knowledge graph completion via embedding-contrastive learning kg. | ZJU | Knowl. Based Syst. | 2022 | [PUB] |
| Federated Graph Learning with Periodic Neighbour Sampling | HKU | IWQoS | 2022 | [PUB] |
| FedGSL: Federated Graph Structure Learning for Local Subgraph Augmentation. | Grandes datos | 2022 | [PUB] | |
| Domain-Aware Federated Social Bot Detection with Multi-Relational Graph Neural Networks. | UCAS; CAS | IJCNN | 2022 | [PUB] |
| A Federated Multi-Server Knowledge Graph Embedding Framework For Link Prediction. | ICTAI | 2022 | [PUB] | |
| A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy | Ping An Technology | KSEM | 2022 | [PUB] [PDF] |
| Clustered Graph Federated Personalized Learning. | NTNU | IEEECONF | 2022 | [PUB] |
| Investigating the Predictive Reproducibility of Federated Graph Neural Networks using Medical Datasets. | MICCAI Workshop | 2022 | [PDF] [CODE] | |
| Peer-to-Peer Variational Federated Learning Over Arbitrary Graphs | UCSD | Int. J. Bio Inspired |
