Awesome FL
v2024-beta
Índice
Usamos outro projeto para rastrear automaticamente atualizações em documentos FL, clique em FL-paper-update-tracker se precisar.
Mais itens serão adicionados ao repositório . Sinta-se à vontade para sugerir outros recursos importantes abrindo um relatório de problema, enviando uma solicitação pull ou enviando-me um e-mail @ ([email protected]). Se você deseja se comunicar com mais amigos na área de aprendizagem federada, junte-se ao grupo QQ [联邦学习交流群], o número do grupo é 833638275. Boa leitura!
Aviso de atualização do repositório
2024/09/30
Caros usuários, Gostaríamos de informá-los sobre algumas alterações que afetarão este repositório de código aberto. O proprietário e principal colaborador @youngfish42 concluiu com sucesso seus estudos de doutorado? em 30 de setembro de 2024, e desde então mudou seu foco de pesquisa. Esta mudança nas circunstâncias terá impacto na frequência e extensão das atualizações da lista de documentos do repositório.
Em vez das atualizações regulares anteriores, prevemos que a lista de documentos será agora atualizada mensal ou trimestralmente. Além disso, a profundidade destas atualizações será reduzida. Por exemplo, as atualizações relacionadas à instituição do autor e ao código-fonte aberto não serão mais mantidas ativamente.
Entendemos que isso pode afetar o valor que você deriva deste repositório. Portanto, convidamos humildemente mais colaboradores a participarem da atualização do conteúdo. Este esforço colaborativo garantirá que o repositório continue sendo um recurso valioso para todos.
Agradecemos sua compreensão e esperamos seu apoio e contribuições contínuos.
Atenciosamente,
白小鱼 (peixe jovem)
categorias
Inteligência Artificial (IJCAI, AAAI, AISTATS, ALT, AI)
Aprendizado de Máquina (NeurIPS, ICML, ICLR, COLT, UAI, Aprendizado de Máquina, JMLR, TPAMI)
Mineração de dados (KDD, WSDM)
Seguro (S&P, CCS, Segurança USENIX, NDSS)
Visão Computacional (ICCV, CVPR, ECCV, MM, IJCV)
Processamento de Linguagem Natural (ACL, EMNLP, NAACL, COLING)
Recuperação de Informação (SIGIR)
Banco de dados (SIGMOD, ICDE, VLDB)
Rede (SIGCOMM, INFOCOM, MOBICOM, NSDI, WWW)
Sistema (OSDI, SOSP, ISCA, MLSys, EuroSys, TPDS, DAC, TOCS, TOS, TCAD, TC)
Outros (ICSE, FOCS, STOC)
| Local | 2024-2020 | antes de 2020 |
|---|---|---|
| IJCAI | 24, 23, 22, 21, 20 | 19 |
| AAAI | 24, 23, 22, 21, 20 | - |
| AISTAS | 24, 23, 22, 21, 20 | - |
| Alt. | 22 | - |
| IA (J) | 23 | - |
| NeuroIPS | 24, 23, 22, 21, 20 | 18, 17 |
| ICML | 24, 23, 22, 21, 20 | 19 |
| ICLR | 24, 23, 22, 21, 20 | - |
| POTRO | 23 | - |
| AIU | 23, 22, 21 | - |
| Aprendizado de Máquina (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 |
| Segurança USENIX | 23, 22, 20 | - |
| NDSS | 24, 23, 22, 21 | - |
| CVPR | 24, 23, 22, 21 | - |
| ICCV | 23,21 | - |
| ECCV | 24, 22, 20 | - |
| Milímetros | 24, 23, 22, 21, 20 | - |
| IJCV (J) | 24 | - |
| LCA | 23, 22, 21 | 19 |
| NAACL | 24, 22, 21 | - |
| EMNLP | 24, 23, 22, 21, 20 | - |
| COLAGEM | 20 | - |
| SIGIR | 24, 23, 22, 21, 20 | - |
| SIGMOD | 22, 21 | - |
| ICDE | 24, 23, 22, 21 | - |
| VLDB | 23, 22, 21, 21, 20 | - |
| SIGCOM | - | - |
| 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 | - |
| DAC | 24, 22, 21 | - |
| TOCS | - | - |
| Termos de Serviço | - | - |
| TCAD | 24, 23, 22, 21 | - |
| TC | 24, 23, 22, 21 | - |
| ICSE | 23, 21 | - |
| FOCOS | - | - |
| Estoque | - | - |
palavras-chave
Estatísticas: o código está disponível e estrelas >= 100 | citação >= 50 | ? Local de primeira linha
kg. : Gráfico de conhecimento | data. : conjunto de dados | surv. : enquete
Artigos de aprendizagem federada na Nature (e suas sub-revistas), Cell, Science (e Science Advances) e PANS referem-se ao mecanismo de busca WOS.
| Título | Afiliação | Local | Ano | Materiais |
|---|---|---|---|---|
| MatSwarm: computação confiável de materiais orientados à aprendizagem por transferência de enxame para compartilhamento seguro de big data | USDB; NTU | Nat. Comum. | 2024 | [PUB] [CÓDIGO] |
| Apresentando inteligência de ponta para medidores inteligentes por meio de aprendizagem dividida federada | HKU | Nat. Comum. | 2024 | [PUB] [新闻] |
| Um estudo internacional apresentando uma plataforma federada de IA de aprendizagem para tumores cerebrais pediátricos | Universidade de Stanford | Nat. Comum. | 2024 | [PUB] [CÓDIGO] |
| PPML-Omics: um método federado de aprendizado de máquina que preserva a privacidade e protege a privacidade dos pacientes em dados ômicos | KAUST | Avanços da Ciência | 2024 | [PUB] [CÓDIGO] |
| A aprendizagem federada não é uma panaceia para a ética dos dados | TUM; UVA | Nat. Mach. Intel.(Comentário) | 2024 | [PUB] |
| Modelo de aprendizagem robustamente federado para identificar pacientes de alto risco com recorrência pós-operatória de câncer gástrico | Hospital Central de Jiangmen; Universidade de Tecnologia Aeroespacial de Guilin; Universidade de Tecnologia Eletrônica de Guilin; | Nat. Comum. | 2024 | [PUB] [CÓDIGO] |
| Compartilhamento seletivo de conhecimento para destilação federada que preserva a privacidade sem um bom professor | HKUST | Nat. Comum. | 2024 | [PUB] [PDF] [CÓDIGO] |
| Um sistema federado de aprendizagem para oncologia de precisão na Europa: DigiONE | IQVIA Cancer Research B.V. | Nat. Med. (Comentário) | 2024 | [PUB] |
| Computação quântica cega distribuída multicliente com a arquitetura Qline | Universidade Sapienza de Roma | Nat. Comum. | 2023 | [PUB] [PDF] |
| Prova de conhecimento zero aprimorada com aleatoriedade quântica independente de dispositivo | USTC | PNAS | 2023 | [PUB] [PDF] [新闻] |
| Classificação colaborativa e com preservação da privacidade de baterias desativadas para reciclagem direta lucrativa por meio de aprendizado de máquina federado | Universidade Tsinghua | Nat. Comum. | 2023 | [PUB] |
| Defendendo a privacidade dos neurodados e a regulamentação da neurotecnologia | Universidade de Columbia | Nat. Protocolo. (Perspectiva) | 2023 | [PUB] |
| Benchmarking federado de inteligência artificial médica com MedPerf | IHU Estrasburgo; Universidade de Estrasburgo; Instituto do Câncer Dana-Farber; Medicina Weill Cornell; Escola de Saúde Pública Harvard TH Chan; MIT; Informações | Nat. Mach. Intel. | 2023 | [PUB] [PDF] [CÓDIGO] |
| Justiça algorítmica em inteligência artificial para medicina e saúde | Escola Médica de Harvard; Broad Institute de Harvard e Instituto de Tecnologia de Massachusetts; Instituto do Câncer Dana-Farber | Nat. Biomédica. Eng. (Perspectiva) | 2023 | [PUB] [PDF] |
| Transferência de conhecimento diferencialmente privado para aprendizagem federada | QUI | Nat. Comum. | 2023 | [PUB] [CÓDIGO] |
| Aprendizagem federada descentralizada por meio de compartilhamento de modelo proxy | Camada 6 IA; Universidade de Waterloo; Instituto de Vetores | Nat. Comum. | 2023 | [PUB] [PDF] [CÓDIGO] |
| Aprendizado de máquina federado em pesquisas compatíveis com proteção de dados | Universidade de Hamburgo | Nat. Mach. Intel.(Comentário) | 2023 | [PUB] |
| Aprendizagem federada para prever a resposta histológica à quimioterapia neoadjuvante no câncer de mama triplo negativo | Owkin | Nat. Med. | 2023 | [PUB] [CÓDIGO] |
| A aprendizagem federada permite big data para detecção de limites de câncer raro | Universidade da Pensilvânia | Nat. Comum. | 2022 | [PUB] [PDF] [CÓDIGO] |
| Aprendizagem federada e soberania de dados genômicos indígenas | Abraçando o rosto | Nat. Mach. Intel. (Comentário) | 2022 | [PUB] |
| Aprendizagem de representação desembaraçada federada para detecção não supervisionada de anomalias cerebrais | TUM | Nat. Mach. Intel. | 2022 | [PUB] [PDF] [CÓDIGO] |
| Mudando o aprendizado de máquina para a área da saúde, do desenvolvimento à implantação e dos modelos aos dados | Universidade de Stanford; Greenstone Biociências | Nat. Biomédica. Eng. (Artigo de revisão) | 2022 | [PUB] |
| Uma estrutura de rede neural gráfica federada para personalização que preserva a privacidade | QUI | Nat. Comum. | 2022 | [PUB] [CÓDIGO] [解读] |
| Aprendizagem federada com eficiência de comunicação por meio da destilação de conhecimento | QUI | Nat. Comum. | 2022 | [PUB] [PDF] [CÓDIGO] |
| Liderar aprendizagem neuromórfica federada para inteligência artificial de borda sem fio | XMU; NTU | Nat. Comum. | 2022 | [PUB] [CÓDIGO] [解读] |
| Uma nova abordagem de aprendizagem federada descentralizada para treinar dados médicos privados protegidos, distribuídos globalmente e de baixa qualidade | Universidade de Wollongong | Ciência. Representante. | 2022 | [PUB] |
| Avançando no diagnóstico da COVID-19 com colaboração que preserva a privacidade em inteligência artificial | HUST | Nat. Mach. Intel. | 2021 | [PUB] [PDF] [CÓDIGO] |
| Aprendizagem federada para prever resultados clínicos em pacientes com COVID-19 | Radiologia MGH e Harvard Medical School | Nat. Med. | 2021 | [PUB] [CÓDIGO] |
| Interferência adversária e suas mitigações no aprendizado de máquina colaborativo que preserva a privacidade | Colégio Imperial de Londres; TUM; OpenMined | Nat. Mach. Intel.(Perspectiva) | 2021 | [PUB] |
| Swarm Learning para aprendizado de máquina clínico descentralizado e confidencial | DZNE; Universidade de Bona; | Natureza ? | 2021 | [PUB] [CÓDIGO] [SOFTWARE] [解读] |
| Privacidade de ponta a ponta preservando o aprendizado profundo em imagens médicas multiinstitucionais | TUM; Colégio Imperial de Londres; OpenMined | Nat. Mach. Intel. | 2021 | [PUB] [CÓDIGO] [解读] |
| Aprendizagem federada com eficiência de comunicação | CUHK; Universidade de Princeton | PANELAS. | 2021 | [PUB] [CÓDIGO] |
| Quebrando fronteiras de compartilhamento de dados médicos usando radiografias sintetizadas | Universidade RWTH de Aachen | Ciência. Avanços. | 2020 | [PUB] [CÓDIGO] |
| Aprendizado de máquina federado, seguro e que preserva a privacidade em imagens médicas | TUM; Colégio Imperial de Londres; OpenMined | Nat. Mach. Intel.(Perspectiva) | 2020 | [PUB] |
Artigos de Aprendizagem Federada aceitos pelas principais conferências e periódicos de IA (Inteligência Artificial), incluindo IJCAI (Conferência Internacional Conjunta sobre Inteligência Artificial), AAAI (Conferência AAAI sobre Inteligência Artificial), AISTATS (Inteligência Artificial e Estatística), ALT (Conferência Internacional sobre Aprendizagem Algorítmica Teoria), IA (Inteligência Artificial).
| Título | Afiliação | Local | Ano | Materiais |
|---|---|---|---|---|
| Clustering multivisualização federado por meio de fatoração de tensor | IJCAI | 2024 | [PUB] | |
| Clustering multivisualização federado eficiente com fatoração de matriz integrada e médias K | IJCAI | 2024 | [PUB] | |
| LG-FGAD: uma estrutura eficaz de detecção de anomalias em grafos federados | IJCAI | 2024 | [PUB] | |
| Aprendizado de prompt federado para modelos da Weather Foundation em dispositivos | IJCAI | 2024 | [PUB] | |
| Quebrando barreiras de heterogeneidade do sistema: aprendizagem federada multimodal tolerante e retardada por meio da destilação de conhecimento | IJCAI | 2024 | [PUB] | |
| Desaprendizagem durante a aprendizagem: um método eficiente de desaprendizagem de máquina federada | IJCAI | 2024 | [PUB] | |
| Compressão de gradiente híbrida prática para sistemas de aprendizagem federados | IJCAI | 2024 | [PUB] | |
| Descoberta causal federada com reconhecimento de heterogeneidade de qualidade de amostra por meio de seleção adaptativa de espaço variável | IJCAI | 2024 | [PUB] [CÓDIGO] | |
| Aprendizagem Federada Regularizada de Norma de Recurso: Utilizando Disparidades de Dados para Ganhos de Desempenho do Modelo | IJCAI | 2024 | [PUB] [CÓDIGO] | |
| Quantificação de incerteza baseada em Dirichlet para aprendizagem federada personalizada com redes posteriores aprimoradas | IJCAI | 2024 | [PUB] | |
| FedConPE: bandidos conversacionais federados eficientes com clientes heterogêneos | IJCAI | 2024 | [PUB] | |
| DarkFed: um ataque backdoor sem dados no aprendizado federado | IJCAI | 2024 | [PUB] | |
| Desaprendizado federado escalonável por meio de fragmentação isolada e codificada | IJCAI | 2024 | [PUB] | |
| Aprimorando a recomendação entre domínios de destino duplo com aprendizagem federada que preserva a privacidade | IJCAI | 2024 | [PUB] | |
| Vazamento de rótulo na aprendizagem federada vertical: uma pesquisa | IJCAI | 2024 | [PUB] | |
| A ascensão da inteligência federada: dos modelos de fundação federada à inteligência coletiva | IJCAI | 2024 | [PUB] | |
| LEAP: Otimização da aprendizagem federada hierárquica em dados não IID com jogo de formação de coalizão | IJCAI | 2024 | [PUB] | |
| EAB-FL: Exacerbando o preconceito algorítmico por meio de ataques de envenenamento de modelo na aprendizagem federada | IJCAI | 2024 | [PUB] | |
| Destilação de Conhecimento na Aprendizagem Federada: Um Guia Prático | IJCAI | 2024 | [PUB] | |
| FedGCS: uma estrutura generativa para seleção eficiente de clientes em aprendizagem federada por meio de otimização baseada em gradiente | IJCAI | 2024 | [PUB] | |
| FedPFT: ajuste fino de proxy federado de modelos básicos | IJCAI | 2024 | [PUB] [CÓDIGO] | |
| Uma pesquisa sistemática sobre aprendizagem semissupervisionada federada | IJCAI | 2024 | [PUB] | |
| Agentes inteligentes para aprendizagem federada baseada em leilões: uma pesquisa | IJCAI | 2024 | [PUB] | |
| Uma estratégia de lances de maximização de receita sem preconceitos para consumidores de dados em aprendizagem federada baseada em leilões | IJCAI | 2024 | [PUB] | |
| Aprendizagem federada personalizada baseada em calibração dupla | IJCAI | 2024 | [PUB] | |
| Apoio à decisão orientado às partes interessadas para aprendizagem federada baseada em leilões | IJCAI | 2024 | [PUB] | |
| Redefinindo contribuições: aprendizagem federada orientada por Shapley | IJCAI | 2024 | [PUB] [CÓDIGO] | |
| Uma pesquisa sobre métodos eficientes de aprendizagem federada para treinamento de modelo básico | IJCAI | 2024 | [PUB] | |
| Da otimização à generalização: aprendizagem federada justa contra mudança de qualidade por meio da correspondência de nitidez entre clientes | IJCAI | 2024 | [PUB] [CÓDIGO] | |
| FBLG: Uma abordagem baseada em gráfico local para lidar com dados não-IID com distorção dupla na aprendizagem federada | IJCAI | 2024 | [PUB] | |
| FedFa: um paradigma de treinamento totalmente assíncrono para aprendizagem federada | IJCAI | 2024 | [PUB] | |
| FedSSA: agregação baseada em similaridade semântica para aprendizagem federada personalizada e heterogênea de modelo eficiente | IJCAI | 2024 | [PUB] | |
| FedES: parada antecipada federada para impedir a memorização de ruídos heterogêneos de rótulos | IJCAI | 2024 | [PUB] | |
| Aprendizado federado personalizado para previsão de tráfego entre cidades | IJCAI | 2024 | [PUB] | |
| Adaptação Federada para Recomendações Baseadas em Modelo Básico | IJCAI | 2024 | [PUB] | |
| BADFSS: ataques backdoor à aprendizagem auto-supervisionada federada | IJCAI | 2024 | [PUB] | |
| Estimando antes da eliminação de preconceitos: uma abordagem bayesiana para eliminar preconceitos anteriores na aprendizagem semissupervisionada federada | IJCAI | 2024 | [PUB] [CÓDIGO] | |
| FedTAD: Destilação de conhecimento livre de dados com reconhecimento de topologia para aprendizagem federada de subgrafo | IJCAI | 2024 | [PUB] | |
| BOBA: Aprendizagem Federada Robusta Bizantina com Distorção de Rótulos | UIUC | AISTAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| Bandidos Contextuais Lineares Federados com Clientes Heterogêneos | Universidade da Virgínia | AISTAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| Projeto de experimento federado sob privacidade diferencial distribuída | Universidade de Stanford; meta | AISTAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| Escapando de pontos de sela na aprendizagem federada heterogênea por meio de SGD distribuído com compactação de comunicação | Universidade de Princeton | AISTAS | 2024 | [PUB] [PDF] |
| SGD assíncrono em gráficos: uma estrutura unificada para otimização assíncrona, descentralizada e federada | INRIA | AISTAS | 2024 | [PUB] [PDF] |
| SIFU: Desaprendizado Federado Informado Sequencial para Desaprendizado de Cliente Eficiente e Comprovável em Otimização Federada | INRIA | AISTAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| Compactação com distribuição exata de erros para aprendizado federado | École Polytechnique | AISTAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| Otimização Minimax Federada Adaptativa com Complexidades Menores | NJU; Laboratório principal do MIIT de análise de padrões e inteligência de máquina | AISTAS | 2024 | [PUB] [PDF] |
| Compressão adaptativa em aprendizagem federada por meio de informações secundárias | Universidade de Stanford; Universidade de Pádua | AISTAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| Aprendizado federado sob demanda para distribuições arbitrárias de classes alvo | UNIST | AISTAS | 2024 | [PUB] [CÓDIGO] |
| FedFisher: aproveitando as informações da Fisher para aprendizagem federada única | UMC | AISTAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| Dinâmica de filas de aprendizagem federada assíncrona | Huawei | AISTAS | 2024 | [PUB] [PDF] |
| Bandido Armado X Federado Personalizado | Universidade Purdue | AISTAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| Aprendizagem federada para registros eletrônicos de saúde heterogêneos utilizando redes de atenção de gráficos temporais aumentadas | Universidade de Oxford | AISTAS | 2024 | [PUB] [CÓDIGO] |
| Ascensão de descida gradiente suavizada estocástica para otimização Federated Minimax | Universidade da Virgínia | AISTAS | 2024 | [PUB] [PDF] |
| Compreendendo a generalização da aprendizagem federada por meio de estabilidade: a heterogeneidade é importante | Universidade do Noroeste | AISTAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| Benefícios mútuos prováveis da aprendizagem federada em domínios sensíveis à privacidade | Universidade de Sófia | AISTAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| Análise de vazamento de privacidade em modelos federados de grandes linguagens | Universidade da Flórida | AISTAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| Agregador invariável para defesa contra ataques backdoor federados | UIUC | AISTAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| Aprendizagem federada com comunicação eficiente com heterogeneidade de dados e clientes | ISTA | AISTAS | 2024 | [PUB] [PDF] [CÓDIGO] |
| FedMut: Aprendizagem Federada Generalizada via Mutação Estocástica | NTU | AAAI | 2024 | [PUB] |
| Aprendizado de rótulo parcial federado com aumento e regularização adaptativa local | Universidade Carleton | AAAI | 2024 | [PUB] [PÁGINA] |
| Sem preconceito! Redes Neurais de Gráficos Federados Justos para Recomendação Personalizada | IIT | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Lógica formal habilitada para aprendizado federado personalizado por meio de inferência de propriedades | Universidade Vanderbilt | AAAI | 2024 | [PUB] [PDF] |
| Aprendizagem de representação com preservação de privacidade independente de tarefas para aprendizagem federada contra ataques de inferência de atributos | Tecnologia de Illinois | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| FairTrade: Alcançando trade-offs ideais de Pareto entre precisão equilibrada e justiça na aprendizagem federada | Universidade Leibniz | AAAI | 2024 | [PUB] [PÁGINA] |
| Combatendo desequilíbrios de dados na aprendizagem semissupervisionada federada com reguladores duplos | HKUST | AAAI | 2024 | [PUB] [PÁGINA] [PDF] |
| Fed-QSSL: uma estrutura para aprendizagem federada personalizada sob largura de bits e heterogeneidade de dados | EUA | AAAI | 2024 | [PUB] [PÁGINA] [PDF] |
| Sobre o desemaranhamento da transferência assimétrica de conhecimento para aprendizagem federada agnóstica de tarefa de modalidade | Universidade da Virgínia | AAAI | 2024 | [PUB] |
| FedDAT: uma abordagem para ajuste fino do modelo básico em aprendizagem federada heterogênea multimodal | LMU Munique Siemens AG | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Cuidado com a cabeça: montando cabeças de projeção para salvar a confiabilidade dos modelos federados | Laboratório-chave conjunto de Shaanxi da Universidade Xi'an Jiaotong para inteligência artificial | AAAI | 2024 | [PUB] [PÁGINA] [PDF] |
| FedGCR: Alcançando Desempenho e Justiça para Aprendizagem Federada com Tipos de Clientes Distintos por meio de Personalização e Reponderação de Grupo | NTU | AAAI | 2024 | [PUB] [PÁGINA] [CÓDIGO] |
| Codificadores Federados Específicos de Modalidade e Âncoras Multimodais para Segmentação Personalizada de Tumores Cerebrais | Universidade de Xiamen | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Explorando distorções de rótulo no aprendizado federado com concatenação de modelo | NUS | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Destilação de conhecimento complementar para modelo robusto e que preserva a privacidade, servindo na aprendizagem federada vertical | SUST; HKUST | AAAI | 2024 | [PUB] [PÁGINA] |
| Aprendizagem Federada via Destilação Colaborativa de Entrada-Saída | Universidade de Búfalo; Escola Médica de Harvard nos EUA | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Aprendizagem Federada de Uma Rodada Calibrada com Inferência Bayesiana no Espaço Preditivo | Instituto de Vetores da Universidade de Waterloo | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| FedCSL: uma abordagem escalonável e precisa para aprendizagem de estrutura causal federada | HFU | AAAI | 2024 | [PUB] [PDF] |
| FedFixer: Mitigando ruído de rótulo heterogêneo no aprendizado federado | Universidade Xi'an Jiaotong; Universidade de Leiden | AAAI | 2024 | [PUB] [PÁGINA] [PDF] |
| FedLPS: aprendizagem federada heterogênea para múltiplas tarefas com compartilhamento de parâmetros locais | NJU | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Aprendizagem trinível federada comprovadamente convergente | TJU | AAAI | 2024 | [PUB] [PDF] |
| Aprendizagem Federativa Performativa: Uma Solução para Mudanças de Distribuição Heterogêneas e Dependentes de Modelo | UM | AAAI | 2024 | [PUB] [PÁGINA] |
| General Commerce Intelligence: Mecanismo Glocalmente Federado Baseado em PNL para Preservação da Privacidade e Serviços Personalizados Sustentáveis de Multicomerciantes | Universidade Kyung Hee; Harex InfoTech | AAAI | 2024 | [PUB] [PÁGINA] |
| EMGAN: Early-Mix-GAN na extração de modelo do lado do servidor em Split Federated Learning | IA da Sony | AAAI | 2024 | [PUB] [PÁGINA] [CÓDIGO] |
| FedDiv: filtragem colaborativa de ruído para aprendizagem federada com rótulos barulhentos | SYSU; HKU | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Transformador de ponto com aprendizagem federada para prever o status HER2 do câncer de mama a partir de imagens de slides inteiros corados com hematoxilina e eosina | USTC; CAS | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| FedNS: um algoritmo tipo Newton de esboço rápido para aprendizagem federada | CAS | AAAI | 2024 | [PUB] [PDF] [CÓDIGO] |
| Bandido Armado X Federado | Universidade Purdue | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Fundação Algorítmica de Aprendizagem Federada com Dados Sequenciais | GMU | AAAI | 2024 | [PUB] |
| UFDA: Adaptação de Domínio Federado Universal com Suposições Práticas | XJTU; Universidade de Sydney | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| FedASMU: aprendizagem federada assíncrona eficiente com atualização de modelo dinâmico com reconhecimento de estagnação | Hithink RoyalFlush Information Network Co. | AAAI | 2024 | [PUB] [PÁGINA] [PDF] |
| Transformador guiado por linguagem para classificação federada de vários rótulos | NTU | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| FedCD: Aprendizagem semissupervisionada federada com equilíbrio de conscientização de classe por meio de professores duplos | SZU | AAAI | 2024 | [PUB] [PÁGINA] [CÓDIGO] |
| Além das ameaças tradicionais: um ataque backdoor persistente ao aprendizado federado | HEU | AAAI | 2024 | [PUB] [PÁGINA] [CÓDIGO] |
| Aprendizagem federada com clientes extremamente barulhentos por meio de destilação negativa | XMU | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| FedST: aprendizagem de transferência de estilo federado para segmentação de imagens não IID | USTB | AAAI | 2024 | [PUB] [PAGE] [学报] [CÓDIGO] |
| PPIDSG: um esquema de compartilhamento de distribuição de imagens que preserva a privacidade com GAN no aprendizado federado | USTC | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Uma estrutura de gêmeo digital cognitivo (CDT) baseada na preservação da privacidade (PPFL) para cidades inteligentes | UDC | AAAI | 2024 | [PUB] |
| Um algoritmo Primal-Dual para aprendizagem federada híbrida | Universidade do Noroeste | AAAI | 2024 | [PUB] [PÁGINA] [PDF] |
| FedLF: Aprendizagem Federada Justa em Camadas | CUHK; Instituto de Inteligência Artificial e Robótica de Shenzhen para a Sociedade | AAAI | 2024 | [PUB] [PÁGINA] |
| Rumo à aprendizagem federada Fair Graph por meio de mecanismos de incentivo | ZJU; FDU | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Rumo à Robustez da Aprendizagem Federada Diferencialmente Privada | QUI | AAAI | 2024 | [PUB] [PÁGINA] |
| Resistindo a ataques backdoor na aprendizagem federada por meio de eleições bidirecionais e perspectiva individual | ZJU; HUAWEI | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Número inteiro é suficiente: quando o aprendizado federado vertical encontra o arredondamento | ZJU; Grupo de Formigas | AAAI | 2024 | [PUB] [PÁGINA] |
| Aprendizagem federada guiada por CLIP sobre heterogeneidade e dados de cauda longa | XMU | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Ajuste de prompt adaptativo federado para aprendizagem colaborativa em vários domínios | FDU | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Aprendizagem Federada Justa Multidimensional | SDU | AAAI | 2024 | [PUB] [PÁGINA] [PDF] |
| HiFi-Gas: Mecanismo de incentivo à aprendizagem federada hierárquica, estimativa aprimorada do uso de gás | Grupo ENN | AAAI | 2024 | [PUB] |
| Sobre o papel do impulso do servidor na aprendizagem federada | Universidade da Virgínia | AAAI | 2024 | [PUB] [PDF] |
| FedCompetitors: Colaboração Harmoniosa na Aprendizagem Federada com Participantes Concorrentes | BUPT | AAAI | 2024 | [PUB] [PÁGINA] [PDF] |
| z-SignFedAvg: uma compactação estocástica unificada baseada em sinais para aprendizagem federada | CUHK; Instituto de Pesquisa de Big Data da China Shenzhen | AAAI | 2024 | [PUB] [PÁGINA] [PDF] |
| Aprendizado federado assíncrono ciente de disparidade de dados e indisponibilidade temporal para manutenção preditiva em frotas de transporte | Grupo Volkswagen | AAAI | 2024 | [PUB] [PÁGINA] |
| Aprendizagem de grafos federados sob mudança de domínio com protótipos generalizáveis | QU | AAAI | 2024 | [PUB] [PÁGINA] |
| TurboSVM-FL: impulsionando o aprendizado federado por meio da agregação SVM para clientes preguiçosos | Universidade Técnica de Munique | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Minimização de discrepância de gradiente colaborativo de múltiplas fontes para generalização de domínio federado | TJU | AAAI | 2024 | [PUB] [PDF] [CÓDIGO] |
| Ocultando amostras sensíveis contra vazamento de gradiente no aprendizado federado | Universidade Monash | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| FedA3I: agregação com reconhecimento de qualidade de anotação para segmentação federada de imagens médicas contra ruído de anotação heterogênea | HUST | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| Aprendizagem de causalidade federada com otimização adaptativa explicável | SDU | AAAI | 2024 | [PUB] [PÁGINA] [PDF] |
| Bandidos Federados em Cascata Contextual com Comunicação Assíncrona e Usuários Heterogêneos | USTC | AAAI | 2024 | [PUB] [PÁGINA] [PDF] |
| Explorando a aprendizagem federada semissupervisionada única com modelos de difusão pré-treinados | FDU | AAAI | 2024 | [PUB] [PDF] |
| Estilização co-restrita de diversidade-autenticidade para generalização de domínio federado na reidentificação pessoal | XMU; Universidade de Trento | AAAI | 2024 | [PUB] [PÁGINA] |
| PerFedRLNAS: pesquisa de arquitetura neural federada personalizada e única | U de T | AAAI | 2024 | [PUB] [PÁGINA] |
| Aprendizado federado assíncrono eficiente com agregação de impulso prospectivo e correção refinada | BUPT | AAAI | 2024 | [PUB] [PÁGINA] |
| Ataques adversários em algoritmos de taxa de bits adaptativos aprendidos federados | HKU | AAAI | 2024 | [PUB] |
| FedTGP: Protótipos Globais Treináveis com Aprendizagem Contrastiva Aprimorada por Margem Adaptativa para Heterogeneidade de Dados e Modelos em Aprendizagem Federada | SJTU | AAAI | 2024 | [PUB] [PÁGINA] [PDF] [CÓDIGO] |
| LR-XFL: Aprendizagem Federada Explicável Baseada em Raciocínio Lógico | NTU | AAAI | 2024 | [PUB] [PDF] [CÓDIGO] |
| Uma abordagem de minimização de perdas Huber para aprendizagem federada robusta bizantina | Laboratório de Zhejiang | AAAI | 2024 | [PUB] [PÁGINA] [PDF] |
| Coaching de parâmetros com conhecimento de conhecimento para aprendizagem federada personalizada | Universidade do Nordeste | AAAI | 2024 | [PUB] [PÁGINA] |
| Aprendizagem Federada de Label-Noise com Regularização de Produtos de Diversidade Local | SJTU | AAAI | 2024 | [PUB] [PÁGINA] [SUPP] |
| Agregação Ponderada Adaptada na Aprendizagem Federada (Resumo do Aluno) | UBC | AAAI | 2024 | [PUB] |
| Transferência de Conhecimento via Modelo Compacto em Aprendizagem Federada (Resumo do Aluno) | Universidade de Sydney | AAAI | 2024 | [PUB] [PÁGINA] |
| PICSR: Roteador Cross-Silo Informado por Protótipo para Aprendizagem Federada (Resumo do Aluno) | Laboratório Auton da Universidade Estadual de Ohio, CMU | AAAI | 2024 | [PUB] [PÁGINA] |
| Rede de convolução de gráficos com preservação de privacidade para recomendação de itens federados | SZU | IA | 2023 | [PUB] |
| Win-Win: uma estrutura federada que preserva a privacidade para recomendação de vários domínios de destino duplo | CAS; UCAS; Tecnologia JD; Pesquisa de Cidades Inteligentes JD | AAAI | 2023 | [PUB] |
| Ataque não direcionado contra sistemas de recomendação federados por meio de incorporações de itens venenosos e defesa | USTC; Laboratório Chave Estadual de Inteligência Cognitiva | AAAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| Crowdsourcing federado com incentivos | SDU | AAAI | 2023 | [PUB] [PDF] |
| Lidando com a heterogeneidade de dados na aprendizagem federada com protótipos de classe | Universidade de Lehigh | AAAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| FairFed: Habilitando a Justiça do Grupo na Aprendizagem Federada | USC | AAAI | 2023 | [PUB] [PDF] [解读] |
| Propagação de robustez federada: compartilhando robustez adversária na aprendizagem federada heterogênea | Universidade Estadual de Moscou | AAAI | 2023 | [PUB] |
| Esparsificação de complemento: poda de modelo de baixa sobrecarga para aprendizagem federada | NJIT | AAAI | 2023 | [PUB] |
| Comunicação quase gratuita na identificação do melhor braço federado | NUS | AAAI | 2023 | [PUB] [PDF] |
| Agregação de modelo adaptativo em camadas para aprendizagem federada escalável | Universidade do Sul da Califórnia Universidade Inha | AAAI | 2023 | [PUB] [PDF] |
| Envenenamento com Cerberus: ataque backdoor furtivo e conspiratório contra aprendizagem federada | BJTU | AAAI | 2023 | [PUB] |
| FedMDFG: Aprendizagem Federada com Descida Multi-Gradiente e Orientação Justa | CUHK; Instituto de Inteligência Artificial e Robótica para a Sociedade de Shenzhen | AAAI | 2023 | [PUB] |
| Protegendo a agregação segura: mitigando o vazamento de privacidade em várias rodadas no aprendizado federado | USC | AAAI | 2023 | [PUB] [PDF] [VÍDEO] [CÓDIGO] |
| Aprendizagem federada em gráficos não IID por meio de compartilhamento de conhecimento estrutural | UTS | AAAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| Identificação eficiente de similaridade de distribuição na aprendizagem federada em cluster por meio de ângulos principais entre subespaços de dados do cliente | UCSD | AAAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| FedABC: Visando a concorrência leal na aprendizagem federada personalizada | UHU; Laboratório Hubei Luojia; Academia JD Explore | AAAI | 2023 | [PUB] [PDF] |
| Além do ADMM: uma estrutura unificada de aprendizado federado adaptativo com redução de variância do cliente | SUTD | AAAI | 2023 | [PUB] [PDF] |
| FedGS: Amostragem Federada Baseada em Gráfico com Disponibilidade Arbitrária do Cliente | XMU | AAAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| Aprendizagem Federada Adaptativa Mais Rápida | Universidade de Pittsburgh | AAAI | 2023 | [PUB] [PDF] |
| FedNP: Rumo à aprendizagem federada não-IID por meio de propagação neural federada | HKUST | AAAI | 2023 | [PUB] [CÓDIGO] [VÍDEO] [SUPP] |
| Correspondência neural federada bayesiana que completa informações completas | TJU | AAAI | 2023 | [PUB] [PDF] |
| CDMA: um algoritmo prático de aprendizagem federada entre dispositivos para problemas gerais de Minimax | ZJU | AAAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| Modelo generativo federado em dados heterogêneos de múltiplas fontes em IoT | UGS | AAAI | 2023 | [PUB] |
| DeFL: Defesa contra ataques de envenenamento de modelo na aprendizagem federada por meio da conscientização sobre períodos críticos de aprendizagem | SUNY-Universidade de Binghamton | AAAI | 2023 | [PUB] |
| FedALA: Agregação Local Adaptável para Aprendizagem Federada Personalizada | SJTU | AAAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| Investigando a robustez adversária da aprendizagem federada | ZJU | AAAI | 2023 | [PUB] [PDF] |
| Sobre a vulnerabilidade das defesas backdoor para aprendizagem federada | TJU | AAAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| Eco de vizinhos: amplificação de privacidade para aprendizagem federada privada personalizada com modelo Shuffle | RUC; Centro de Pesquisa de Engenharia do Ministério da Educação em Banco de Dados e BI | AAAI | 2023 | [PUB] [PDF] |
| DPAUC: Computação AUC Diferencialmente Privada em Aprendizagem Federada | ByteDance Inc. | Faixas Especiais AAAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| Treinamento eficiente de modelos de diagnóstico de falhas industriais em grande escala por meio de abandono de bloco oportunista federado | NTU | Programas Especiais AAAI | 2023 | [PUB] [PDF] |
| Aprendizagem federada orquestrada em escala industrial para descoberta de medicamentos | KU Leuven | Programas Especiais AAAI | 2023 | [PUB] [PDF] [VÍDEO] |
| Uma ferramenta de monitoramento de aprendizagem federada para simulação de carro autônomo (resumo do aluno) | CNU | Programas Especiais AAAI | 2023 | [PUB] |
| MGIA: Ataque de inversão de gradiente mútuo na aprendizagem federada multimodal (resumo do aluno) | PoliU | Programas Especiais AAAI | 2023 | [PUB] |
| Aprendizagem Federada Clusterizada para Dados Heterogêneos (Resumo do Aluno) | RUC | Programas Especiais AAAI | 2023 | [PUB] |
| FedSampling: uma melhor estratégia de amostragem para aprendizagem federada | QUI | IJCAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| HyperFed: exploração de protótipos hiperbólicos com agregação consistente para dados não IID em aprendizagem federada | ZJU | IJCAI | 2023 | [PUB] [PDF] |
| FedOBD: abandono de bloco oportunista para treinamento eficiente de redes neurais em grande escala por meio de aprendizagem federada | NTU | IJCAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| Modelagem de distribuição de preferência probabilística federada com co-clustering compacto para recomendação de vários domínios com preservação de privacidade | ZJU | IJCAI | 2023 | [PUB] |
| Aprendizagem semântica e estrutural de gráfico federado | QU | IJCAI | 2023 | [PUB] |
| BARA: Mecanismo de incentivo eficiente com alocação de orçamento de recompensa on-line na aprendizagem federada entre silos | SYSU | IJCAI | 2023 | [PUB] [PDF] |
| FedDWA: Aprendizagem Federada Personalizada com Ajuste Dinâmico de Peso | SYSU | IJCAI | 2023 | [PUB] [PDF] |
| FedPass: aprendizagem profunda federada vertical que preserva a privacidade com ofuscação adaptativa | Webank | IJCAI | 2023 | [PUB] [PDF] |
| Autoencoder de gráfico federado globalmente consistente para gráficos não IID | FZU | IJCAI | 2023 | [PUB] [CÓDIGO] |
| Aprendizagem por reforço multiagente competitivo-cooperativa para aprendizagem federada baseada em leilão | NTU | IJCAI | 2023 | [PUB] |
| Personalização dupla em recomendação federada | JLU; Universidade de Tecnologia de Sydney | IJCAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| FedNoRo: Rumo à aprendizagem federada robusta em termos de ruído, abordando o desequilíbrio de classe e a heterogeneidade de ruído de rótulo | HUST | IJCAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| Negação de serviço ou controle refinado: rumo a ataques flexíveis de envenenamento de modelo na aprendizagem federada | Universidade Xiangtan | IJCAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| FedHGN: uma estrutura federada para redes neurais de grafos heterogêneos | CUHK | IJCAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| FedET: uma estrutura de aprendizagem incremental de classe federada com eficiência de comunicação baseada em transformador aprimorado | Tecnologia Ping An; QUI | IJCAI | 2023 | [PUB] [PDF] |
| Prompt de aprendizagem federada para previsão do tempo: em direção a modelos básicos em dados meteorológicos | UTS | IJCAI | 2023 | [PUB] [PDF] [CÓDIGO] |
| FedBFPT: uma estrutura de aprendizagem federada eficiente para o pré-treinamento adicional do Bert | ZJU | IJCAI | 2023 | [PUB] [CÓDIGO] |
| Aprendizagem Federada Bayesiana: Uma Pesquisa | Trilha de pesquisa IJCAI | 2023 | [PDF] | |
| Uma Pesquisa de Avaliação Federada em Aprendizagem Federada | Universidade Macquarie | Trilha de pesquisa IJCAI | 2023 | [PUB] [PDF] |
| SAMBA: Uma Estrutura Genérica para Bandidos Multiarmados Federados Seguros (Resumo Estendido) | INSA Centro Val de Loire | Trilha do Diário IJCAI | 2023 | [PUB] |
| O custo de comunicação de segurança e privacidade na estimativa de frequência federada | Stanford | AISTAS | 2023 | [PUB] [CÓDIGO] |
| Aprendizagem federada eficiente e leve por meio de abandono distribuído assíncrono | Universidade do Arroz | AISTAS | 2023 | [PUB] [CÓDIGO] |
| Aprendizagem federada sob desvio de conceito distribuído | UMC | AISTAS | 2023 | [PUB] [CÓDIGO] |
| Caracterizando ataques de evasão interna na aprendizagem federada | UMC | AISTAS | 2023 | [PUB] [CÓDIGO] |
| Assintóticos Federados: um modelo para comparar algoritmos de aprendizagem federados | Stanford | AISTAS | 2023 | [PUB] [CÓDIGO] |
| Aprendizagem federada privada não convexa sem um servidor confiável | USC | AISTAS | 2023 | [PUB] [CÓDIGO] |
| Aprendizagem federada para fluxos de dados | Universidade e Cˆ ote d'Azur | AISTAS | 2023 | [PUB] [CÓDIGO] |
| Nada além de arrependimentos – Descoberta causal federada que preserva a privacidade | Centro Helmholtz para Segurança da Informação | AISTAS | 2023 | [PUB] [CÓDIGO] |
| Ataque de inferência de associação ativa sob privacidade diferencial local no aprendizado federado | UFL | AISTAS | 2023 | [PUB] [CÓDIGO] |
| Dinâmica de Média Federada de Langevin: Rumo a uma teoria unificada e novos algoritmos | CMAP | AISTAS | 2023 | [PUB] |
| Aprendizagem federada robusta e bizantina com taxas estatísticas ideais | Universidade da Califórnia em Berkeley | AISTAS | 2023 | [PUB] [CÓDIGO] |
| Aprendizagem federada em gráficos não IID por meio de compartilhamento de conhecimento estrutural | UTS | AAAI | 2023 | [PDF] [CÓDIGO] |
| FedGS: Amostragem Federada Baseada em Gráfico com Disponibilidade Arbitrária do Cliente | XMU | AAAI | 2023 | [PDF] [CÓDIGO] |
| Crowdsourcing federado com incentivos | SDU | AAAI | 2023 | [PDF] |
| Para compreender a seleção tendenciosa de clientes na aprendizagem federada. | UMC | AISTAS | 2022 | [PUB] [CÓDIGO] |
| FLIX: uma alternativa simples e eficiente em termos de comunicação aos métodos locais de aprendizagem federada | KAUST | AISTAS | 2022 | [PUB] [PDF] [CÓDIGO] |
| Limites precisos para média federada (SGD local) e perspectiva contínua. | Stanford | AISTAS | 2022 | [PUB] [PDF] [CÓDIGO] |
| Aprendizagem por reforço federado com heterogeneidade ambiental. | PKU | AISTAS | 2022 | [PUB] [PDF] [CÓDIGO] |
| Detecção de comunidade míope federada com comunicação única | Purdue | AISTAS | 2022 | [PUB] [PDF] |
| Algoritmos assíncronos de limite de confiança superior para bandidos lineares federados. | Universidade da Virgínia | AISTAS | 2022 | [PUB] [PDF] [CÓDIGO] |
| Rumo ao aprendizado da estrutura de rede bayesiana federada com otimização contínua. | UMC | AISTAS | 2022 | [PUB] [PDF] [CÓDIGO] |
| Aprendizagem federada com agregação assíncrona com buffer | Meta IA | AISTAS | 2022 | [PUB] [PDF] [VÍDEO] |
| Aprendizagem Federada Diferencialmente Privada em Dados Heterogêneos. | Stanford | AISTAS | 2022 | [PUB] [PDF] [CÓDIGO] |
| SparseFed: Mitigando ataques de envenenamento de modelo na aprendizagem federada com esparsificação | Princeton | AISTAS | 2022 | [PUB] [PDF] [CÓDIGO] [VÍDEO] |
| A base é importante: métodos de segunda ordem com melhor eficiência na comunicação para aprendizagem federada | KAUST | AISTAS | 2022 | [PUB] [PDF] |
| Aumento de gradiente funcional federado. | Universidade da Pensilvânia | AISTAS | 2022 | [Pub] [PDF] [Código] |
| QLSD: Dinâmica estocástica quantizada de Langevin para o aprendizado federado bayesiano. | Laboratório Criteo AI | Aistats | 2022 | [Pub] [PDF] [Código] [Vídeo] |
Extrapolação de conhecimento baseada em meta-aprendizagem para gráficos de conhecimento na configuração federada kg. | Zju | Ijcai | 2022 | [Pub] [PDF] [Código] |
| Aprendizagem federada personalizada com um gráfico | UTS | Ijcai | 2022 | [Pub] [PDF] [Código] |
| Rede neural de gráficos federados verticalmente para classificação de nó de preservação de privacidade | Zju | Ijcai | 2022 | [Pub] [PDF] |
| Adapte-se à adaptação: Aprendendo a personalização do aprendizado federado de Silo Cross | Ijcai | 2022 | [Pub] [PDF] [Código] | |
| Transferência de conhecimento heterogênea para o treinamento de grandes modelos em aprendizado federado | Ijcai | 2022 | [Pub] [PDF] | |
| Aprendizagem federada semi-supervisionada privada. | Ijcai | 2022 | [PUB] | |
| Aprendizagem federada contínua com base na destilação do conhecimento. | Ijcai | 2022 | [PUB] | |
| Aprendizagem federada sobre dados heterogêneos e de cauda longa por meio de re-treinamento de classificadores com recursos federados | Ijcai | 2022 | [Pub] [PDF] [Código] | |
| Atenção federada de várias tarefas para o reconhecimento de atividade humana entre indivíduos | Ijcai | 2022 | [PUB] | |
| Aprendizagem federada personalizada com generalização contextualizada. | Ijcai | 2022 | [Pub] [PDF] | |
| Aprendizagem federada para proteger: agregação robusta com seleção adaptativa de clientes. | Ijcai | 2022 | [Pub] [PDF] | |
| FedCG: alavancar GaN condicional para proteger a privacidade e manter o desempenho competitivo na aprendizagem federada | Ijcai | 2022 | [Pub] [PDF] [Código] | |
| FedDuap: Aprendizagem federada com atualização dinâmica e poda adaptativa usando dados compartilhados no servidor. | Ijcai | 2022 | [Pub] [PDF] | |
surv. | Ijcai | 2022 | [Pub] [PDF] | |
| Harmofl: Harmonizando os desvios locais e globais na aprendizagem federada em imagens médicas heterogêneas | CuHK; Buaa | Aaai | 2022 | [Pub] [PDF] [Código] [解读] |
| Aprendizagem federada para reconhecimento de rosto com correção de gradiente | Putt | Aaai | 2022 | [Pub] [PDF] |
| Spreadgnn: Aprendizagem federada de várias tarefas descentralizada para redes neurais gráficas em dados moleculares | USC | Aaai | 2022 | [Pub] [PDF] [Código] [解读] |
| SmartIDX: reduzindo o custo de comunicação no aprendizado federado, explorando as estruturas CNNs | BATER; Pcl | Aaai | 2022 | [Pub] [Código] |
| Ponte entre sinais de processamento cognitivo e características linguísticas por meio de uma rede atencional unificada | TJU | Aaai | 2022 | [Pub] [PDF] |
| Aproveitando períodos críticos de aprendizagem no aprendizado federado | Universidade SUNY-Binghamton | Aaai | 2022 | [Pub] [PDF] |
| Momento de coordenação para o aprendizado federado de silolos cruzados | Universidade de Pittsburgh | Aaai | 2022 | [Pub] [PDF] |
| FedProto: Aprendizagem de protótipo federada sobre dispositivos heterogêneos | UTS | Aaai | 2022 | [Pub] [PDF] [Código] |
| Fedsoft: aprendizado federado em cluster suave com atualização local proximal | CMU | Aaai | 2022 | [Pub] [PDF] [Código] |
| Treinamento esparso dinâmico federado: calculando menos, comunicando menos, mas aprendendo melhor | A Universidade do Texas em Austin | Aaai | 2022 | [Pub] [PDF] [Código] |
| Fedfr: estrutura federada de otimização conjunta para reconhecimento de rosto genérico e personalizado | Universidade Nacional de Taiwan | Aaai | 2022 | [Pub] [PDF] [Código] |
| SPLITFED: Quando o aprendizado federado atende ao aprendizado dividido | CSIRO | Aaai | 2022 | [Pub] [PDF] [Código] |
| Programação eficiente de dispositivos com aprendizado federado com vários jobs | Universidade de Soochow | Aaai | 2022 | [Pub] [PDF] |
| Alinhamento de gradiente implícito na aprendizagem distribuída e federada | IIT Kanpur | Aaai | 2022 | [Pub] [PDF] |
| Classificação vizinha mais próxima federada com uma colônia de frutas | IBM Research | Aaai | 2022 | [Pub] [PDF] [Código] |
| Campos vetoriais e conservadorismo iterados, com aplicações à aprendizagem federada. | Alt. | 2022 | [Pub] [PDF] | |
| Aprendizagem federada com privacidade e otimização adaptativa amplificadas por escarificação e adaptável | Ijcai | 2021 | [Pub] [PDF] [Vídeo] | |
| Distribuição de imitações de comportamento: combinando comportamentos individuais e em grupo para a aprendizagem federada | Ijcai | 2021 | [Pub] [PDF] | |
| FedSpeech: Federated Text-to-fala com aprendizado contínuo | Ijcai | 2021 | [Pub] [PDF] | |
| Aprendizagem federada prática de um tiro para configuração cruzada | Ijcai | 2021 | [Pub] [PDF] [Código] | |
| Destilação do modelo federado com privacidade diferencial sem ruído | Ijcai | 2021 | [Pub] [PDF] [Vídeo] | |
| LDP-FL: agregação privada prática em aprendizado federado com privacidade diferencial local | Ijcai | 2021 | [Pub] [PDF] | |
| Aprendizagem federada com média justa. | Ijcai | 2021 | [Pub] [PDF] [Código] | |
| H-FL: Uma arquitetura hierárquica eficiente em comunicação e privacidade para a aprendizagem federada. | Ijcai | 2021 | [Pub] [PDF] | |
| Aprendizagem de borda federada descentralizada eficiente e escalável de comunicação. | Ijcai | 2021 | [PUB] | |
| Aprendizagem federada vertical assíncrona segura de Bilevel com atualização reversa | Universidade Xidiana; JD Tech | Aaai | 2021 | [Pub] [PDF] [Vídeo] |
| Fedrec ++: Recomendação federada sem perdas com feedback explícito | Szu | Aaai | 2021 | [Pub] [Vídeo] |
| Bandidos multi-armados federados | Universidade da Virgínia | Aaai | 2021 | [Pub] [PDF] [Código] [Vídeo] |
| Sobre a convergência de SGD local com eficiência de comunicação para aprendizado federado | Universidade Temple; Universidade de Pittsburgh | Aaai | 2021 | [Pub] [Vídeo] |
| Chama: aprendizado federado diferencialmente privado no modelo Shuffle | Universidade Renmin da China; Universidade de Kyoto | Aaai | 2021 | [Pub] [PDF] [Vídeo] [Código] |
| Para entender a influência de clientes individuais na aprendizagem federada | Sjtu; A Universidade do Texas em Dallas | Aaai | 2021 | [Pub] [PDF] [Vídeo] |
| Aprendizagem federada comprovadamente segura contra clientes maliciosos | Universidade Duke | Aaai | 2021 | [Pub] [PDF] [Vídeo] [Slide] |
| Aprendizagem federada de cross-silo personalizada em dados não IID | Universidade Simon Fraser; Universidade McMaster | Aaai | 2021 | [Pub] [PDF] [Vídeo] [UC.] |
| Jogos de compartilhamento de modelos: Analisando o aprendizado federado sob participação voluntária | Universidade de Cornell | Aaai | 2021 | [Pub] [PDF] [Código] [Vídeo] |
| Maldição ou redenção? Como a heterogeneidade dos dados afeta a robustez do aprendizado federado | Universidade de Nevada; IBM Research | Aaai | 2021 | [Pub] [PDF] [Vídeo] |
| Jogo de gradientes: mitigando clientes irrelevantes em aprendizado federado | IIT Bombaim; IBM Research | Aaai | 2021 | [Pub] [PDF] [código] [Vídeo] [Supp] |
| Esquema de descendência de coordenadas em bloco federado para aprender modelos globais e personalizados | CuHK; Universidade Estadual do Arizona | Aaai | 2021 | [Pub] [PDF] [Vídeo] [Código] |
| Abordando o desequilíbrio da classe no aprendizado federado | Universidade do Noroeste | Aaai | 2021 | [Pub] [PDF] [Vídeo] [Código] [解读] |
| Defendendo contra backdoors em aprendizado federado com taxa de aprendizado robusta | A Universidade do Texas em Dallas | Aaai | 2021 | [Pub] [PDF] [Vídeo] [Código] |
| Ataques de piloto livre à agregação de modelos na aprendizagem federada | Accenture Labs | Aistats | 2021 | [Pub] [PDF] [código] [Vídeo] [Supp] |
| Privacidade federada de diferencial F. | Universidade da Pensilvânia | Aistats | 2021 | [Pub] [Código] [Vídeo] [Supp] |
| Aprendizagem federada com compressão: análise unificada e garantias nítidas | A Universidade Estadual da Pensilvânia; A Universidade do Texas em Austin | Aistats | 2021 | [Pub] [PDF] [código] [Vídeo] [Supp] |
| Modelo embaralhado de privacidade diferencial em aprendizado federado | UCLA; Google | Aistats | 2021 | [Pub] [Vídeo] [Supp] |
| Convergência e compensação de precisão em aprendizado e meta-aprendizagem federados | Aistats | 2021 | [Pub] [PDF] [Vídeo] [Supp] | |
| Bandidos multi-armados federados com personalização | Universidade da Virgínia; A Universidade Estadual da Pensilvânia | Aistats | 2021 | [Pub] [PDF] [código] [Vídeo] [Supp] |
| Para a participação flexível do dispositivo na aprendizagem federada | Cmu; Sysu | Aistats | 2021 | [Pub] [PDF] [Vídeo] [Supp] |
| Meta-aprendizagem federada para detecção fraudulenta de cartão de crédito | Ijcai | 2020 | [Pub] [Vídeo] | |
| Um jogo de vários jogadores para estudar esquemas de incentivos de aprendizagem federados | Ijcai | 2020 | [Pub] [Código] [解读] | |
| Árvores de decisão práticas para aumentar o gradiente federado | Nus; Uwa | Aaai | 2020 | [Pub] [PDF] [Código] |
| Aprendizagem federada para problemas de aterramento de visão e linguagem | Pku; Tencent | Aaai | 2020 | [PUB] |
| Alocação de Dirichlet latente federada: uma estrutura local baseada em privacidade diferencial | Buaa | Aaai | 2020 | [PUB] |
| Hash federado de pacientes | Universidade de Cornell | Aaai | 2020 | [PUB] |
| Aprendizagem federada robusta por meio do ensino de máquinas colaborativas | Symantec Research Labs; Kaust | Aaai | 2020 | [Pub] [PDF] |
| FedVision: uma plataforma de detecção de objetos visuais on -line alimentada pela aprendizagem federada | Webank | Aaai | 2020 | [Pub] [PDF] [Código] |
| FedPaq: um método de aprendizado federado com eficiência de comunicação com média periódica e quantização | UC Santa Barbara; Ut Austin | Aistats | 2020 | [Pub] [PDF] [Vídeo] [Supp] |
| Como fazer backdoor de aprendizagem federada | Cornell Tech | Aistats | 2020 | [Pub] [PDF] [Vídeo] [Code] [Supp] |
| Descoberta de rebatedores pesados federados com privacidade diferencial | Rpi; Google | Aistats | 2020 | [Pub] [PDF] [Vídeo] [Supp] |
| Visualização multi-agente para explicar o aprendizado federado | Webank | Ijcai | 2019 | [Pub] [Vídeo] |
Documentos de aprendizagem federados aceitos pela ML (Machine Learning) Conference and Journal, incluindo Neurips (Conferência Anual sobre Sistemas de Processamento de Informações Neurais), ICML (Conferência Internacional sobre Aprendizagem de Máquina), ICLR (Conferência Internacional sobre Representações de Aprendizagem), Colt (Conferência Anual Computacional Teoria da aprendizagem), UAI (conferência sobre incerteza em inteligência artificial), Machine Learning, JMLR (Journal of Machine Learning Research), TPAMI (IEEE Transactions na análise de padrões e inteligência de máquina).
| Título | Afiliação | Local | Ano | Materiais |
|---|---|---|---|---|
| Estabilizar e acelerar o aprendizado federado em dados heterogêneos com participação parcial do cliente | Tpami | 2025 | [PUB] | |
| Modelo federado médico com mistura de componentes personalizados e compartilhados | Tpami | 2025 | [PUB] | |
| Aprendizagem federada de um tiro por meio de comunicação sintética do destilador-distilador | Neurips | 2024 | [PUB] | |
| Aprendizagem federada não -convexa em submanifolds suaves compactos com dados heterogêneos | Neurips | 2024 | [PUB] | |
| FedGMKD: um protótipo eficiente da estrutura de aprendizagem federada por meio de destilação de conhecimento e agregação consciente da discrepância | Neurips | 2024 | [PUB] | |
| Melhorando a generalização no aprendizado federado com a regularização de informações mútuas de dados de dados: uma abordagem de inferência posterior | Neurips | 2024 | [PUB] | |
| Modelo Federado | Neurips | 2024 | [PUB] | |
| Learning de gráfico federado para recomendação de domínio cruzado | Neurips | 2024 | [PUB] | |
| Fedgmark: Marcar aquático certificável para a aprendizagem federada de gráficos | Neurips | 2024 | [PUB] | |
| Adaptador de personalização dupla para modelos de fundação federados | Neurips | 2024 | [PUB] | |
| Gradiente de Política Nural Federada e Métodos Críticos de Ator para Aprendizagem de Reforço de Multas Tarefas | Neurips | 2024 | [PUB] | |
| Dominando a cauda longa na previsão de mobilidade humana | Neurips | 2024 | [PUB] | |
| Defesa dupla: melhorando a privacidade e mitigando ataques de envenenamento no aprendizado federado | Neurips | 2024 | [PUB] | |
| Otimizadores aprimorados por gráficos para recomendação de reconhecimento de estrutura evolução | Neurips | 2024 | [PUB] | |
| Dofit: Instrução Federada com reconhecimento de domínio Tuning com esquecimento catastrófico aliviado | Neurips | 2024 | [PUB] | |
| Aprendizagem federada eficiente contra a indisponibilidade heterogênea e não estacionária | Neurips | 2024 | [PUB] | |
| Transformador federado: aprendizado federado vertical multipartidário em dados práticos ligados | Neurips | 2024 | [PUB] | |
| FIARSE: Aprendizagem federada heterogênica de modelo por meio de extração de submodelo com consciência de importância | Neurips | 2024 | [PUB] | |
| Pronto de prontidão federada probabilística com dados não IID e desequilibrados | Neurips | 2024 | [PUB] | |
| Flora: Modelos de linguagem grande e feminina federados com adaptações heterogêneas de baixo rank | Neurips | 2024 | [PUB] | |
| Dominando a variação de representação entre domínios na aprendizagem federada de protótipo com domínios de dados heterogêneos | Neurips | 2024 | [PUB] | |
| PFEDClub: agregação de modelo heterogêneo controlável para aprendizado federado personalizado | Neurips | 2024 | [PUB] | |
| Por que ficar cheio? Elevar o aprendizado federado por meio de atualizações parciais de rede | Neurips | 2024 | [PUB] | |
| Fusefl: Aprendizagem federada de um tiro através da lente da causalidade com fusão progressiva do modelo | Neurips | 2024 | [PUB] | |
| FEDSSP: Aprendizagem de gráficos federados com conhecimento espectral e preferência personalizada | Neurips | 2024 | [PUB] | |
| Lidando com Learnwares de espaços de características heterogêneas com exploração explícita de rótulo | Neurips | 2024 | [PUB] | |
| A-FEDPD: Alinhando a deriva dupla é todas as necessidades federadas de aprendizado primal-dual | Neurips | 2024 | [PUB] | |
| Estimativa de frequência privada e personalizada em um ambiente federado | Neurips | 2024 | [PUB] | |
| A compensação da complexidade da amostra-comunicação no q-learning federado | Neurips | 2024 | [PUB] | |
| Aprendizagem de reforço offline dirigido por conjuntos federados | Neurips | 2024 | [PUB] | |
| Adaptação de caixa preta federada para segmentação semântica | Neurips | 2024 | [PUB] | |
| Pensando em frente: Finetuning federado com economia de memória de modelos de idiomas | Neurips | 2024 | [PUB] | |
| Aprendizagem federada com modelos de fundação em linguagem da visão: análise teórica e método | Neurips | 2024 | [PUB] | |
| Design ideal para elicitação de preferência humana | Neurips | 2024 | [PUB] | |
| Para diversos dispositivos, aprendizado federado heterogêneo por meio de integração de conhecimento aritmético de tarefas | Neurips | 2024 | [PUB] | |
| Aprendizado federado personalizado por meio de adaptação para distribuição de recursos | Neurips | 2024 | [PUB] | |
| Scafflsa: Heterogeneidade domesticada na aproximação estocástica linear federada e aprendizado de TD | Neurips | 2024 | [PUB] | |
| Uma abordagem bayesiana para aprendizado federado personalizado em ambientes heterogêneos | Neurips | 2024 | [PUB] | |
| RFLPA: uma estrutura robusta de aprendizagem federada contra ataques de envenenamento com agregação segura | Neurips | 2024 | [PUB] | |
| FedGTST: Aumentando a transferibilidade global de modelos federados por meio de ajuste estatística | Neurips | 2024 | [PUB] | |
| Cluster de ponta a ponta para aprendizado de intenção em recomendação | Neurips | 2024 | [PUB] | |
| Fedlpa: aprendizado federado com um tiro com agregação posterior em camada | Neurips | 2024 | [PUB] | |
| Time-FFM: Rumo ao modelo de fundação federado com LM-EM POWERED para previsão de séries temporais | Neurips | 2024 | [PUB] | |
| Foogd: colaboração federada para generalização e detecção fora da distribuição | Neurips | 2024 | [PUB] | |
| Uma faca do exército suíço para aprendizado federado heterogêneo: acoplamento flexível via norma de rastreamento | Neurips | 2024 | [PUB] | |
| Fedne: vizinha federada assistida por substitutos para redução de dimensionalidade | Neurips | 2024 | [PUB] | |
| O treinamento local de baixa precisão é suficiente para o aprendizado federado | Neurips | 2024 | [PUB] | |
| Aprendizagem auto-supervisionada federada com consciência de recursos com representações de classe global | Neurips | 2024 | [PUB] | |
| Sobre a necessidade de colaboração para seleção de modelos on -line com dados descentralizados | Neurips | 2024 | [PUB] | |
| O poder da extrapolação no aprendizado federado | Neurips | 2024 | [PUB] | |
| (FL) $^2 $: Superando poucas gravadoras em aprendizado semi-supervisionado federado | Neurips | 2024 | [PUB] | |
| Sobre estratégias de amostragem para o modelo de modelo espectral | Neurips | 2024 | [PUB] | |
| Personalizando modelos de linguagem com Lora para recomendação seqüencial | Neurips | 2024 | [PUB] | |
| Spafl: aprendizado federado com eficiência de comunicação com modelos esparsos e baixa computacional | Neurips | 2024 | [PUB] | |
| HYDRA-FL: Destilação do conhecimento híbrido para aprendizado federado robusto e preciso | Neurips | 2024 | [PUB] | |
| Métodos de ponto proximal estabilizado para otimização federada | Neurips | 2024 | [PUB] | |
| Dapperfl: Aprendizagem federada adaptativa de domínio com a poda de fusão de modelos para dispositivos de borda | Neurips | 2024 | [PUB] | |
| Dissecção de disparidades de parâmetros para defesa de backdoor em aprendizado federado heterogêneo | Neurips | 2024 | [PUB] | |
| O agente de pior desempenho lidera o pacote? Analisando a dinâmica do agente em SGD distribuído unificado | Neurips | 2024 | [PUB] | |
| Fedavp: Aumentar os dados locais por meio de política compartilhada em aprendizado federado | Neurips | 2024 | [PUB] | |
| Cobo: Aprendizagem colaborativa via otimização de Bilevel | Neurips | 2024 | [PUB] | |
| Análise de convergência do aprendizado federado dividido em dados heterogêneos | Neurips | 2024 | [PUB] | |
| Grupo federado com eficiência de comunicação Otimização robusta distributamente | Neurips | 2024 | [PUB] | |
| Ferrari: Desejo federado desaprendendo através da otimização da sensibilidade dos recursos | Neurips | 2024 | [PUB] | |
| Aprendizagem federada sobre os modos conectados | Neurips | 2024 | [PUB] | |
| Aprendizagem federada personalizada com mistura de modelos para previsão adaptativa e modelo de ajuste fino | Neurips | 2024 | [PUB] | |
| A justiça igualitária leva à instabilidade? Os limites de justiça em aprendizado federado estável sob comportamentos altruístas | Neurips | 2024 | [PUB] | |
| Previsão on-line federada de especialistas com privacidade diferencial: separações e acelerações de arrependimento | Neurips | 2024 | [PUB] | |
| DataStealing: roubar dados de modelos de difusão em aprendizado federado com vários troianos | Neurips | 2024 | [PUB] | |
| Planos comportamentais federados: explicando a evolução do comportamento do cliente na aprendizagem federada | Neurips | 2024 | [PUB] | |
| Aprendizagem federada hierárquica com correção de gradiente em várias termos de escala | Neurips | 2024 | [PUB] | |
| Hiperprismo: uma estrutura de agregação não linear adaptativa para aprendizado de máquina distribuído em relação a dados não IID e links de comunicação variáveis no tempo | Neurips | 2024 | [PUB] | |
| Lança: inversão exata de gradiente de lotes em aprendizado federado | Neurips | 2024 | [PUB] | |
| Aprendizagem federada sob participação periódica do cliente e dados heterogêneos: um novo algoritmo e análise com eficiência de comunicação | Neurips | 2024 | [PUB] | |
| Lacunas de ponte: agrupamento federado de várias visões em visualizações híbridas heterogêneas | Neurips | 2024 | [PUB] | |
| Aprendizagem federada resistente à confusão através da harmonização de dados baseada em difusão em dados não IID | Neurips | 2024 | [PUB] | |
| Sopas superiores locais: um catalisador para a fusão de modelos no aprendizado federado de silolos | Neurips | 2024 | [PUB] | |
| Formação de colaboração com conscientização de conflitos e conflitos para aprendizado federado com silos | Neurips | 2024 | [PUB] | |
| Cluster de classificador e alinhamento de recursos para aprendizado federado sob deriva de conceito distribuído | Neurips | 2024 | [PUB] | |
| Amostragem de clientes guiados por heterogeneidade: para aprendizado federado rápido e eficiente não IID | Neurips | 2024 | [PUB] | |
| Fato ou ficção: Os mecanismos verdadeiros podem eliminar a pilotagem livre federada? | Neurips | 2024 | [PUB] | |
| Aprendizagem de preferência ativa para encomendar itens dentro e fora da amostra | Neurips | 2024 | [PUB] | |
| Ajuste federal federado de grandes modelos de linguagem sob tarefas heterogêneas e recursos do cliente | Neurips | 2024 | [PUB] | |
| Personalização de ajuste fino em aprendizado federado para mitigar clientes adversários | Neurips | 2024 | [PUB] | |
| Revisitando conjuntos de aprendizado federado de um tiro | Neurips | 2024 | [PUB] | |
| Fedllm-banco: benchmarks realistas para aprendizado federado de grandes modelos de idiomas | Neurips | 2024 | [PUB] | |
| $ exttt {pfl-research} $: estrutura de simulação para acelerar pesquisas em aprendizado federado privado | Neurips | 2024 | [PUB] | |
| Fedmeki: uma referência para escalar modelos de fundação médica por meio de injeção de conhecimento federada | Neurips | 2024 | [PUB] | |
| Momentum Aproximação em aprendizado federado privado assíncrono | Workshop Neurips | 2024 | [PUB] | |
| Squeeze de coorte: além de uma única rodada de comunicação por coorte em aprendizado federado de dispositivos cruzados | Workshop Neurips | 2024 | [PUB] | |
| Aprendizagem federada com conteúdo generativo | Workshop Neurips | 2024 | [PUB] | |
| Aproveitando dados de texto não estruturados para o ajuste federado de instrução de modelos de linguagem grande | Workshop Neurips | 2024 | [PUB] | |
| Ataque emergente de segurança e defesa no ajuste federado de instruções de grandes modelos de idiomas | Workshop Neurips | 2024 | [PUB] | |
| Colaboração sem deserção entre concorrentes em um sistema de aprendizado | Workshop Neurips | 2024 | [PUB] | |
| Nas taxas de convergência de q-learning federados em ambientes heterogêneos | Workshop Neurips | 2024 | [PUB] | |
| CLUSTER: trazendo criptografia funcional em modelos fundamentais federados | Workshop Neurips | 2024 | [PUB] | |
| Ferreto: ajuste federado de parâmetro completo em escala para modelos de idiomas grandes | Workshop Neurips | 2024 | [PUB] | |
| Aprendizagem federada e federada quente | Workshop Neurips | 2024 | [PUB] | |
| Treinamento dinâmico de baixo rank federado com garantias de convergência de perdas globais | Workshop Neurips | 2024 | [PUB] | |
| O futuro do grande modelo de linguagem pré-treinamento é federado | Workshop Neurips | 2024 | [PUB] | |
| Aprendizagem colaborativa com representações lineares compartilhadas: taxas estatísticas e algoritmos ideais | Workshop Neurips | 2024 | [PUB] | |
| O fenômeno sinapticcity: quando todos os modelos de fundação se casam com aprendizado e blockchain federados | Workshop Neurips | 2024 | [PUB] | |
| Zoopfl: Explorando modelos de fundação de caixa preta para aprendizado federado personalizado | Workshop Neurips | 2024 | [PUB] | |
| Decomfl: aprendizado federado com comunicação sem dimensão | Workshop Neurips | 2024 | [PUB] | |
| Melhorando a conectividade do grupo para generalização do aprendizado profundo federado | Workshop Neurips | 2024 | [PUB] | |
| Mapa: Modelo se fundindo com a frente de Pareto amortizada usando computação limitada | Workshop Neurips | 2024 | [PUB] | |
| OPA: agregação privada com uma tiro com interação única do cliente e seus aplicativos para o aprendizado federado | Workshop Neurips | 2024 | [PUB] | |
| Pirataria híbrida adaptativa na aprendizagem federada por meio da exploração de perdas | Workshop Neurips | 2024 | [PUB] | |
| Treinamento federado em todo o mundo dos modelos de idiomas | Workshop Neurips | 2024 | [PUB] | |
| Fedstein: Aprimorando o aprendizado federado de vários domínios através do estimador James-Stein | Workshop Neurips | 2024 | [PUB] | |
| Melhorando a descoberta causal em ambientes federados com amostras locais limitadas | Workshop Neurips | 2024 | [PUB] | |
| $ exttt {pfl-research} $: estrutura de simulação para acelerar pesquisas em aprendizado federado privado | Workshop Neurips | 2024 | [PUB] | |
| DMM: Mecanismo de matriz distribuído para aprendizado federado de diferencial, usando compartilhamento secreto embalado | Workshop Neurips | 2024 | [PUB] | |
| FedCbo: alcançando consenso em grupo em aprendizado federado em cluster por meio de otimização baseada em consenso | Jmlr | 2024 | [PUB] | |
| Combinação de gráficos federados eficazes | ICML | 2024 | [PUB] | |
| Entendendo o aprendizado federado assistido pelo servidor na presença de participação incompleta do cliente | ICML | 2024 | [PUB] | |
| Além da Federação: Aprendizagem Federada de Alexo de Topologia para Generalização para clientes invisíveis | ICML | 2024 | [PUB] | |
| FedBPT: Eficiente Federated Black-Box Prompt Suning para modelos de idiomas grandes | ICML | 2024 | [PUB] | |
| Bridging Model Heterogeneity na aprendizagem federada por meio de aprendizado de reciprocidade assimétrica baseada em incerteza | ICML | 2024 | [PUB] | |
| Uma nova perspectiva teórica sobre a heterogeneidade de dados na otimização federada | ICML | 2024 | [PUB] | |
| Melhorando o armazenamento e a eficiência computacional no aprendizado multimodal federado para modelos em larga escala | ICML | 2024 | [] | |
| Momento para a vitória: aprendizado colaborativo de reforço federado em ambientes heterogêneos | ICML | 2024 | [PUB] | |
| Aprendizagem federada bizantina-robust: impacto da subamostragem de clientes e atualizações locais | ICML | 2024 | [PUB] | |
| Benefícios comprováveis das etapas locais em aprendizado federado heterogêneo para redes neurais: uma perspectiva de aprendizado de recursos | ICML | 2024 | [PUB] | |
| Acelerando o aprendizado federado com estimativa média distribuída rápida | ICML | 2024 | [PUB] | |
| Aprendizagem federada justa através do núcleo de veto proporcional | ICML | 2024 | [PUB] | |
| AEGISFL: Eficiente e flexível que preserva a preservação bizantina-robust Cross-Silo Federated Learning | ICML | 2024 | [PUB] | |
| Recuperação de etiquetas de atualizações locais no aprendizado federado | ICML | 2024 | [PUB] | |
| Fedmbridge: aprendizagem federada multimodal ponte | ICML | 2024 | [PUB] | |
| Harmonizando a generalização e personalização no aprendizado imediato federado | ICML | 2024 | [PUB] | |
| Perturbações globais estimadas localmente são melhores que as perturbações locais para a minimização federada de reconhecimento de nitidez | ICML | 2024 | [PUB] | |
| Aprendizagem federada heterogênea acelerando com classificadores de forma fechada | ICML | 2024 | [PUB] | |
| Federados Bandidos Multi-Agentes Federados | ICML | 2024 | [PUB] | |
| Um método duplamente recursivo de descida de gradiente de composição estocástica para otimização de composição multinível federada | ICML | 2024 | [PUB] | |
| Aprendizagem federada heterogênea privada sem um servidor confiável revisitado: algoritmos ideais e com eficiência de comunicação para perdas convexas | ICML | 2024 | [PUB] | |
| FedRC: combater diversas distribuições de distribuição desafios no aprendizado federado por cluster robusto | ICML | 2024 | [PUB] | |
| Perseguindo o bem -estar geral no aprendizado federado por meio de tomada de decisão seqüencial | ICML | 2024 | [PUB] | |
| Pré-texto: Modelos de idiomas de treinamento em dados federados privados na era do LLMS | ICML | 2024 | [PUB] | |
| Agregação de entropia autodidata para o aprendizado federado heterogêneo bizantino-robusto | ICML | 2024 | [PUB] | |
| Superando os dados e heterogeneidades de modelos em aprendizado federado descentralizado por meio de âncoras sintéticas | ICML | 2024 | [PUB] | |
| Otimização federada com correção duplamente regularizada de deriva | ICML | 2024 | [PUB] | |
| FedSC: Aprendizagem auto-supervisionada federada comprovável com objetivo contrastante espectral sobre dados não IID | ICML | 2024 | [PUB] | |
| Previsão conforme federada bizantina-robusta-robusta | ICML | 2024 | [PUB] | |
| Alcançar a escarsificação de gradiente sem perdas via mapeamento para espaço alternativo no aprendizado federado | ICML | 2024 | [PUB] | |
| Aprendizagem federada em cluster via particionamento baseado em gradiente | ICML | 2024 | [PUB] | |
| Saídas precoces recorrentes para aprendizado federado com clientes heterogêneos | ICML | 2024 | [PUB] | |
| Repensando a pesquisa de mínimos planos na aprendizagem federada | ICML | 2024 | [PUB] | |
| Fedbat: aprendizado federado com eficiência de comunicação por meio de binarização aprendida | ICML | 2024 | [PUB] | |
| Aprendizagem de representação federada no regime sub-parametizado | ICML | 2024 | [PUB] | |
| Fedlmt: Sistema de combate heterogeneidade da aprendizagem federada por meio de treinamento de modelos de baixo rank com garantias teóricas | ICML | 2024 | [PUB] | |
| Algoritmo de reconhecimento de ruído para aprendizado federado diferencialmente privado heterogêneo | ICML | 2024 | [PUB] | |
| Prata: Redução e aplicação de variação de loop único à aprendizagem federada | ICML | 2024 | [PUB] | |
| Signsgd com defesa federada: aproveitando ataques adversários por meio de decodificação de sinal de gradiente | ICML | 2024 | [PUB] | |
| FedCal: alcançando a calibração local e global em aprendizado federado por meio de scaler parametrizado agregado | ICML | 2024 | [PUB] | |
| Aprendizagem contínua federada por meio de transferência de conhecimento duplo baseado | ICML | 2024 | [PUB] | |
| Ajuste federado de parâmetros completos de modelos de idiomas de bilhão com custo de comunicação abaixo de 18 kilobytes | ICML | 2024 | [PUB] | |
| Maximização submodular decomposta em configuração federada | ICML | 2024 | [PUB] | |
| Otimização estocástica privada e federada: estratégias eficientes para sistemas centralizados | ICML | 2024 | [PUB] | |
| Modelagem aprimorada de conjuntos de dados federados usando misturas de Dirichlet-Multinomials | ICML | 2024 | [PUB] | |
| Lições da análise de erros de generalização do aprendizado federado: você pode se comunicar com menos frequência! | ICML | 2024 | [PUB] | |
| Aprendizagem federada resiliente e rápida e federada | ICML | 2024 | [PUB] | |
| Aprendizagem invariável federada personalizada motivada causalmente com a regularização teórica da informação de atalho | ICML | 2024 | [PUB] | |
| Seleção de imitação de clientes baseada em classificação para aprendizado federado eficiente | ICML | 2024 | [PUB] | |
| Rumo à teoria da aprendizagem federada não supervisionada: análise não asintótica de algoritmos EM federados | ICML | 2024 | [PUB] | |
| FADAS: Rumo a otimização assíncrona adaptativa federada | ICML | 2024 | [PUB] | |
| Aprendizagem de reforço offline federado: cobertura colaborativa de políticas únicas é suficiente | ICML | 2024 | [PUB] | |
| FedreDeFense: defender contra ataques de envenenamento por modelo para aprendizado federado usando o modelo de reconstrução de atualização do modelo | ICML | 2024 | [PUB] | |
| MH-PFLID: Modelo de aprendizado federado personalizado heterogêneo por meio de injeção e destilação para análise de dados médicos | ICML | 2024 | [PUB] | |
| Aprendizagem neuro-simbólica federada | ICML | 2024 | [PUB] | |
| Personalização de grupo adaptável para aprendizado de transferência mútua federada | ICML | 2024 | [PUB] | |
| Equilibrando a similaridade e a complementaridade para a aprendizagem federada | ICML | 2024 | [PUB] | |
| GNNs de explicação auto-exagerada federada com aumentos anti-shortcut | ICML | 2024 | [PUB] | |
| Um algoritmo de minimax composicional estocástico federado para maximização da AUC profunda | ICML | 2024 | [PUB] | |
| Coala: uma plataforma de aprendizado federada prática e centrada na visão | ICML | 2024 | [PUB] | |
| Aprendizagem federada vertical assíncrona segura e rápida via otimização híbrida em cascata | Mach aprenda | 2024 | [PUB] | |
| Aprendizagem federada em cluster eficiente em comunicação por meio de distância do modelo | Ustc; Laboratório Chave do Estado de Inteligência Cognitiva | Mach aprenda | 2024 | [PUB] |
| Aprendizagem federada com agregação superquantil para dados heterogêneos. | Pesquisa do Google | Mach aprenda | 2024 | [Pub] [PDF] [Código] |
| Alinhando saídas do modelo para aprendizado federado não IID desequilibrado | NJU | Mach aprenda | 2024 | [PUB] |
| Aprendizagem federada de redes causais lineares generalizadas | Tpami | 2024 | [PUB] | |
| Reconhecimento de atividade humana federada | Tpami | 2024 | [PUB] | |
| Processo Gaussiano Federado: Convergência, Personalização Automática e Modelagem de Multi-Fidelidade | Universidade do Nordeste; Uom | Tpami | 2024 | [Pub] [PDF] [Código] |
| O impacto dos ataques adversários à aprendizagem federada: uma pesquisa | IIT | Tpami | 2024 | [PUB] |
| Entendendo e atenuando o colapso dimensional na aprendizagem federada | NUS | Tpami | 2024 | [Pub] [PDF] [Código] |
| Ninguém deixou para trás: aprendizado federado de classe federado no mundo real | Cas; Ucas | Tpami | 2024 | [Pub] [PDF] [Código] |
| Generalizável heterogêneo federado correlação e aprendizado de similaridade de instância | QU | Tpami | 2024 | [Pub] [PDF] [Código] |
| Aprendizagem federada assíncrona de vários estágios com privacidade diferencial adaptativa | Hpu; Xjtu | Tpami | 2024 | [Pub] [PDF] [Código] |
| Uma estrutura de aprendizado federada bayesiana com aproximação on -line | Sustech | Tpami | 2024 | [Pub] [PDF] [Código] |
| Aprimorando o aprendizado federado de um tiro por meio de dados e co-impulsionamento de dados | Ustc; Hkbu | ICLR | 2024 | [PUB] |
| Estimativa de privacidade empírica de um tiro para aprendizado federado | ICLR | 2024 | [Pub] [PDF] | |
| Média controlada estocástica para aprendizado federado com compactação de comunicação | LinkedIn; Upenn | ICLR | 2024 | [Pub] [PDF] |
| Um método leve para combater estatísticas de participação desconhecidas na média federada | IBM | ICLR | 2024 | [Pub] [PDF] [Código] |
| Uma perspectiva de informação mútua sobre o aprendizado contrastante federado | Qualcomm | ICLR | 2024 | [PUB] |
| Algoritmos de benchmarking para generalização do domínio federada | Universidade de Purdue | ICLR | 2024 | [Pub] [PDF] [Código] |
| Aprendizagem de árvores federadas eficaz e eficiente em dados híbridos | UC Berkeley | ICLR | 2024 | [Pub] [PDF] |
| Recomendação federada com personalização aditiva | UTS | ICLR | 2024 | [Pub] [PDF] [Código] |
| Combatendo a heterogeneidade de dados em aprendizado federado assíncrono com calibração de atualização em cache | Psu | ICLR | 2024 | [Pub] [Supp] |
| Treinamento ortogonal federado: mitigando esquecimento catastrófico global em aprendizado contínuo federado | USC | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Esquecimento preciso para o aprendizado contínuo federado heterogêneo | QUI | ICLR | 2024 | [Pub] [Código] |
| Descoberta causal federada de dados heterogêneos | Mbzuai | ICLR | 2024 | [Pub] [PDF] [Código] |
| Em bandidos contextuais lineares federados diferencialmente privados | Wayne State University | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Comunicação verdadeira incentivada para bandidos federados | Universidade da Virgínia | ICLR | 2024 | [Pub] [PDF] |
| Adaptação de domínio federada de princípios: projeção de gradiente e peso automático | Uiuc | ICLR | 2024 | [PUB] |
| FedP3: Federada Personalizada e Prungem a Rede Amigável de Privacidade sob heterogeneidade do Modelo | Kaust | ICLR | 2024 | [PUB] |
| Geração rápida orientada por texto para modelos de linguagem de visão em aprendizado federado | Robert Bosch LLC | ICLR | 2024 | [Pub] [PDF] |
| Melhorando a Lora em aprendizado federado que preserva a privacidade | Northeastern University | ICLR | 2024 | [PUB] |
| Fedwon: Triunfando aprendizado federado com vários domínios sem normalização | Sony AI | ICLR | 2024 | [Pub] [PDF] |
| FedTrans: Estimativa de utilidade transparente do cliente para aprendizado federado robusto | Tu Delft | ICLR | 2024 | [PUB] |
| FedComPass: Aprendizagem federada e eficiente do Silo Cross-Silo em dispositivos de clientes heterogêneos usando um agendador de computação com consciência de energia | ANL; Uiuc; NCSA | ICLR | 2024 | [Pub] [PDF] [Código] [Página] |
| Otimização de núcleos bayesianos para aprendizado federado personalizado | IIT Bombaim | ICLR | 2024 | [PUB] |
| Conectividade do modo linear em camada | Ruhr-Universtät Bochum | ICLR | 2024 | [Pub] [PDF] [Supp] |
| Fake It Até fazer: aprendizado federado com geração orientada a consenso | Sjtu | ICLR | 2024 | [Pub] [PDF] |
| Escondidos à vista da vista: disfarçar os ataques de roubo de dados no aprendizado federado | Insait | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Análise de tempo finito da aprendizagem de reforço federado heterogêneo na política | Universidade de Columbia | ICLR | 2024 | [Pub] [PDF] |
| Aprendizado federado adaptável com clientes ajustados automaticamente | Universidade de Rice | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Aprendizagem federada de backdoor envenenando camadas críticas de backdoor | DE | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Q-learning federado: aceleração linear de arrependimento com baixo custo de comunicação | Psu | ICLR | 2024 | [Pub] [Supp] [PDF] |
| FedimPro: Medindo e melhorando a atualização do cliente na aprendizagem federada | Hkbu | ICLR | 2024 | [Pub] [PDF] |
| Distância federada de Wasserstein | MIT | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Uma análise aprimorada de recorte por amostra e atualização na aprendizagem federada | Dtu | ICLR | 2024 | [PUB] |
| FedCDA: Aprendizagem Federada com agregação cruzada de divergência de divergência | NTU | ICLR | 2024 | [Pub] [Supp] |
| Gradientes internos de camada cruzada para estender a homogeneidade à heterogeneidade no aprendizado federado | HKU | ICLR | 2024 | [Pub] [PDF] |
| Momentum beneficia o aprendizado federado não IID de maneira simples e provável | Pku; Upenn | ICLR | 2024 | [Pub] [PDF] |
| Otimização de bandidos federados com eficiência de comunicação | Universidade de Yale | ICLR | 2024 | [Pub] [PDF] |
| Avaliação de contribuição justa e eficiente para aprendizado federado vertical | Huawei | ICLR | 2024 | [Pub] [Supp] [PDF] [Código] |
| Desmistificação de compensações de justiça local e global na aprendizagem federada usando a decomposição de informações parciais | UMCP | ICLR | 2024 | [Pub] [PDF] |
| Aprendendo representações personalizadas causalmente invariantes para clientes federados heterogêneos | Polyu | ICLR | 2024 | [PUB] |
| Pefll: aprendizado federado personalizado, aprendendo a aprender | Ist | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Métodos de descida de gradiente eficiente de comunicação para desigualdades variacionais distribuídas: análise unificada e atualizações locais | Jhu | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Fedinverse: Avaliando vazamento de privacidade na aprendizagem federada | USQ | ICLR | 2024 | [Pub] [Supp] |
| Fedda: Métodos de gradiente adaptativo mais rápido para otimização restrita federada | UMCP | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Treinamento robusto de modelos federados com deficiência de rótulo extremamente | Hkbu | ICLR | 2024 | [Pub] [PDF] [Código] |
| Entendendo a convergência e generalização no aprendizado federado através da teoria da aprendizagem de recursos | Riken AIP | ICLR | 2024 | [PUB] |
| Teach LLMS para Phish: roubar informações privadas de modelos de idiomas | Universidade de Princeton | ICLR | 2024 | [PUB] |
| Como óleo e água: métodos de robustez em grupo e defesas de envenenamento não misturam | UMCP | ICLR | 2024 | [PUB] |
| Convergência acelerada do método estocástico de bola pesada sob ruído anisotrópico de gradiente | Hkust | ICLR | 2024 | [Pub] [PDF] |
| Para eliminar restrições de etiquetas duras em ataques de inversão de gradiente | CAS | ICLR | 2024 | [Pub] [Supp] [PDF] [Código] |
| Otimização local de ponto de sela composta | Universidade de Purdue | ICLR | 2024 | [Pub] [PDF] |
| Aumentar o treinamento neural por meio de um modelo de dinâmica correlacionada | Tiit | ICLR | 2024 | [Pub] [PDF] |
| Econtrol: otimização distribuída rápida com compactação e controle de erros | Universidade Saarland | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Construindo exemplos adversários para aprendizagem federada vertical: corrupção ideal do cliente por meio de bandidos com vários braços | Hkust | ICLR | 2024 | [PUB] |
| Fedhyper: um agendador de taxa de aprendizado universal e robusto para aprendizado federado com descendência hipergradiente | UMCP | ICLR | 2024 | [Pub] [Supp] [PDF] [Código] |
| Aprendizagem federada personalizada heterogênea por atualizações locais-globais misturando via taxa de convergência | Cuhk | ICLR | 2024 | [PUB] |
| Quebrando fronteiras físicas e linguísticas: ajuste imediato multilíngue federado para idiomas de baixo recurso | University of Cambridge | ICLR | 2024 | [PUB] |
| Simple Minimax Optimal Byzantine Robust Algorithm for Nonconvex Objectives with Uniform Gradient Heterogeneity | NTT DATA Mathematical Systems Inc. | ICLR | 2024 | [PUB] |
| VFLAIR: A Research Library and Benchmark for Vertical Federated Learning | QUI | ICLR | 2024 | [PUB] [PDF] [CODE] |
| Incentive-Aware Federated Learning with Training-Time Model Rewards | NUS | ICLR | 2024 | [PUB] [SUPP] |
| VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks | NUS | ICLR | 2024 | [PUB] [PDF] [CODE] |
| FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data | ZJU | ICLR | 2024 | [PUB] [SUPP] [PDF] |
| SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning | University at Buffalo | NeurIPS | 2023 | [PUB] [PDF] [SUPP] |
| Mechanism Design for Collaborative Normal Mean Estimation | UW-Madison | NeurIPS | 2023 | [PUB] [PDF] |
| Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity | EPFL | NeurIPS | 2023 | [PUB] [PDF] [CODE] |
| Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization | UIUC | NeurIPS | 2023 | [PUB] [SUPP] |
| Convergence Analysis of Sequential Federated Learning on Heterogeneous Data | BUPT | 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 | University of Virginia | NeurIPS | 2023 | [PUB] [PDF] |
| Multiply Robust Federated Estimation of Targeted Average Treatment Effects | Northeastern University | 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 | University of 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 | PSU | 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 | Western University | 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 | University of 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 | University of Pittsburgh | NeurIPS | 2023 | [PUB] [SUPP] |
| Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems | University of 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 | A Universidade de Sydney | 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 | PSU | 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 | PEDAÇO | 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 | QU | 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 | University of 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 | NTU | 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 | SCU | 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 | Stanford University | 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 | KIT | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation | Stanford University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Incentivizing Honesty among Competitors in Collaborative Learning and Optimization | ETH Zurich | 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 | Stanford University | 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; University of Texas at 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 | University of 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 Zurich | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Strategic Data Sharing between Competitors | Universidade de Sófia | 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 | Pesquisa do Google | 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 | Rensselaer Polytechnic Institute | 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 | Cornell University | Uai | 2023 | [PUB] [PDF] [SUPP] [MATERIAL] [CODE] |
| Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape | A Universidade de Sydney | ICML | 2023 | [PUB] [PDF] [SLIDES] |
| Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression: Fast Convergence and Partial Participation | LinkedIn Ads | 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 | Pesquisa do Google | 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 | NUS | 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 | Universidade de Auburn | 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; NTU | 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 | Texas A&M University | ICML | 2023 | [PUB] [PDF] [CODE] |
| No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation | BATER | 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 Zurich | 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 | Duke University | 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 | QUI | 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 Leuven | ICML | 2023 | [PUB] [PDF] [CODE] |
| Fair yet Asymptotically Equal Collaborative Learning | NUS | 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; NUS | 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 | Rensselaer Polytechnic Institute | ICML | 2023 | [PUB] [PDF] |
| FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks | University of Minnesota | ICML | 2023 | [PUB] |
| Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm | University of 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 | University of 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 | Universidade Hanyang | TPAMI | 2023 | [PUB] [PDF] |
| Efficient Federated Learning Via Local Adaptive Amended Optimizer With Linear Speedup | Universidade de 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 | QUI | 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 | MSU | 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 | NUS | ICLR | 2023 | [PUB] [PDF] [CODE] |
| FedFA: Federated Feature Augmentation | ETH Zurich | 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 | University of 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 | NUS | ICLR | 2023 | [PUB] [CODE] |
| Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning | NUS | 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 | University of Cambridge | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Multimodal Federated Learning via Contrastive Representation Ensemble | QUI | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Faster federated optimization under second-order similarity | Princeton University | ICLR | 2023 | [PUB] [PDF] [CODE] |
| FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy | Universidade de 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 | OSSU | 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 | University of 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 | University of 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 | NUS | Uai | 2022 | [PUB] [PDF] |
| Communication-Efficient Randomized Algorithm for Multi-Kernel Online Federated Learning | Universidade Hanyang | 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 | Universidade 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 | UC Berkeley | NeurIPS | 2022 | [PUB] [CODE] |
| Improved Utility Analysis of Private CountSketch | ITU | 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 | MSU | 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 | QUI | 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? | QU | 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 | OSSU | NeurIPS | 2022 | [PUB] [PDF] |
| Taming Fat-Tailed (“Heavier-Tailed” with Potentially Infinite Variance) Noise in Federated Learning | OSSU | 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 | UC Berkeley | NeurIPS | 2022 | [PUB] [PDF] |
| Byzantine-tolerant federated Gaussian process regression for streaming data | PSU | 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 | University of 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 | Northeastern University | NeurIPS | 2022 | [PUB] [PDF] |
| Resource-Adaptive Federated Learning with All-In-One Neural Composition | JHU | NeurIPS | 2022 | [PUB] |
| Self-Aware Personalized Federated Learning | Amazônia | NeurIPS | 2022 | [PUB] [PDF] |
| A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning | Northeastern University | NeurIPS | 2022 | [PUB] [PDF] |
| An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects | NUS | 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 | Duke University | 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 | University of 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 | Tulane University | 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 | NUS | 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 | University of 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 | NYU | ICML | 2022 | [PUB] [PDF] [CODE] |
| The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning | Stanford; Pesquisa do Google | ICML | 2022 | [PUB] [PDF] [CODE] [SLIDE] |
| The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation | Stanford; Pesquisa do Google | 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 | University of Cambridge | ICML | 2022 | [PUB] [PDF] [SLIDE] [CODE] |
| Accelerated Federated Learning with Decoupled Adaptive Optimization | Universidade de Auburn | ICML | 2022 | [PUB] [PDF] |
| Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling | Georgia Tech | ICML | 2022 | [PUB] [PDF] |
| Multi-Level Branched Regularization for Federated Learning | Seoul National University | ICML | 2022 | [PUB] [PDF] [CODE] [PAGE] |
| FedScale: Benchmarking Model and System Performance of Federated Learning at Scale | University of 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 | University of 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 | The University of Texas at 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 | Universidade 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 | University of 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 | The Ohio State University | 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 | Michigan State University | 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 Zurich | 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; Universidade de Washington | ICLR | 2022 | [PUB] [PDF] |
| Acceleration of Federated Learning with Alleviated Forgetting in Local Training | QUI | 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 | University of Pennsylvania | ICLR | 2022 | [PUB] [CODE] |
| Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization | Michigan State University; The University of Texas at Austin | ICLR | 2022 | [PUB] [PDF] [CODE] |
| Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models | University of Maryland; NYU | ICLR | 2022 | [PUB] [PDF] [CODE] |
| ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity | University of Cambridge; University of 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 | The Ohio State University | 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; BRAÇO | ICLR | 2021 | [PUB] [PDF] [CODE] |
| Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning | The Ohio State University | ICLR | 2021 | [PUB] [PDF] |
| HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients | Duke University | 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 | The Ohio State University | 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 | Harvard University | 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; University of Pennsylvania | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] |
| Data-Free Knowledge Distillation for Heterogeneous Federated Learning | Michigan State University | 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 | Accenture | 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; Braço | ICML | 2021 | [PUB] [CODE] [VIDEO] |
| One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning | Toyota; Berkeley; Cornell University | 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; Amazônia | 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 | NUS | NeurIPS | 2021 | [PUB] [CODE] |
| Gradient Inversion with Generative Image Prior | POSTECH | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Distributed Machine Learning with Sparse Heterogeneous Data | University of 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 | NUS | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Optimality and Stability in Federated Learning: A Game-theoretic Approach | Cornell University | 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 | Bar-Ilan University | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Differentially Private Federated Bayesian Optimization with Distributed Exploration | MIT; NUS | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Parameterized Knowledge Transfer for Personalized Federated Learning | PolyU | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Federated Reconstruction: Partially Local Federated Learning | Pesquisa do Google | NeurIPS | 2021 | [PUB] [PDF] [CODE] [UC.] |
| Fast Federated Learning in the Presence of Arbitrary Device Unavailability | QUI; 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 | University of Pennsylvania | 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; Hewlett Packard Enterprise | 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 | QUI; Alibaba; Weill Cornell Medicine | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Federated Linear Contextual Bandits | The Pennsylvania State University; Facebook; University of 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; Pesquisa do Google | NeurIPS | 2021 | [PUB] [CODE] [VIDEO] |
| Federated-EM with heterogeneity mitigation and variance reduction | Ecole Polytechnique; Pesquisa do Google | NeurIPS | 2021 | [PUB] [PDF] |
| Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning | MIT; Amazon; 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; Amazônia | 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 | UC 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; DeepMind | NeurIPS | 2020 | [PUB] [PDF] [CODE] |
| Distributionally Robust Federated Averaging | Pennsylvania State University | NeurIPS | 2020 | [PUB] [PDF] [CODE] |
| Personalized Federated Learning with Moreau Envelopes | A Universidade de Sydney | 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 | University of 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 | Afiliação | Local | Ano | Materiais |
|---|---|---|---|---|
| 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 | University of 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 | University of 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 | University of 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 | MSU | 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 | QUI | 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 | University of Virginia | KDD | 2022 | [PUB] |
| Communication-Efficient Robust Federated Learning with Noisy Labels | University of 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 | QUI | 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 | Nanjing University | KDD | 2021 | [PUB] [CODE] |
| Federated Adversarial Debiasing for Fair and Trasnferable Representations | Michigan State University | 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 | Duke University | 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 | University College Dublin | 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 | Afiliação | Local | Ano | Materiais |
|---|---|---|---|---|
| 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 | University of Pavia | CCS | 2023 | [PUB] [PDF] |
| MESAS: Poisoning Defense for Federated Learning Resilient against Adaptive Attackers | University of Würzburg | CCS | 2023 | [PUB] |
| martFL: Enabling Utility-Driven Data Marketplace with a Robust and Verifiable Federated Learning Architecture | QUI | 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 | University of 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 | University of Warwick | CCS | 2022 | [PUB] [PDF] [CODE] |
| FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information | Duke University | 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 Zurich | 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 | Fudan University | 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 | Microsoft Research | 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 | NUS | CCS | 2021 | [PUB] [PDF] |
| FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping | Duke University | 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 | University of 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 | The Ohio State University | USENIX Security | 2020 | [PUB] [PDF] [CODE] [VIDEO] [SLIDE] |
| A Reliable and Accountable Privacy-Preserving Federated Learning Framework using the Blockchain | Universidade do 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 | University of 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 | Afiliação | Local | Ano | Materiais |
|---|---|---|---|---|
| DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations | Milímetros | 2024 | [PUB] | |
| One-shot-but-not-degraded Federated Learning | Milímetros | 2024 | [PUB] | |
| Overcoming Spatial-Temporal Catastrophic Forgetting for Federated Class-Incremental Learning | Milímetros | 2024 | [PUB] | |
| FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models | Milímetros | 2024 | [PUB] | |
| Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-rank Decomposition | Milímetros | 2024 | [PUB] | |
| CoAst: Validation-Free Contribution Assessment for Federated Learning based on Cross-Round Valuation | Milímetros | 2024 | [PUB] | |
| Spatio-temporal Heterogeneous Federated Learning for Time Series Classification with Multi-view Orthogonal Training | Milímetros | 2024 | [PUB] | |
| FedEvalFair: A Privacy-Preserving and Statistically Grounded Federated Fairness Evaluation Framework | Milímetros | 2024 | [PUB] | |
| One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity | Milímetros | 2024 | [PUB] | |
| FedSLS: Exploring Federated Aggregation in Saliency Latent Space | Milímetros | 2024 | [PUB] | |
| Cluster-driven Personalized Federated Recommendation with Interest-aware Graph Convolution Network for Multimedia | Milímetros | 2024 | [PUB] | |
| FedBCGD: Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning | Milímetros | 2024 | [PUB] | |
| Federated Morozov Regularization for Shortcut Learning in Privacy Preserving Learning with Watermarked Image Data | Milímetros | 2024 | [PUB] | |
| Cross-Modal Meta Consensus for Heterogeneous Federated Learning | Milímetros | 2024 | [PUB] | |
| Masked Random Noise for Communication-Efficient Federated Learning | Milímetros | 2024 | [PUB] | |
| Heterogeneity-Aware Federated Deep Multi-View Clustering towards Diverse Feature Representations | Milímetros | 2024 | [PUB] | |
| Adaptive Hierarchical Aggregation for Federated Object Detection | Milímetros | 2024 | [PUB] | |
| FedCAFE: Federated Cross-Modal Hashing with Adaptive Feature Enhancement | Milímetros | 2024 | [PUB] | |
| Federated Fuzzy C-means with Schatten-p Norm Minimization | Milímetros | 2024 | [PUB] | |
| Towards Effective Federated Graph Anomaly Detection via Self-boosted Knowledge Distillation | Milímetros | 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 | QU | 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 | HUST | CVPR | 2024 | [PUB] [SUPP] [PDF] |
| Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices | EUA | CVPR | 2024 | [PUB] [SUPP] [PDF] |
| Data Valuation and Detections in Federated Learning | NUS | 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 | DE | 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 | UC Davis | CVPR | 2024 | [PUB] [SUPP] [PDF] |
| FedAS: Bridging Inconsistency in Personalized Federated Learning | QU | 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; AIRS | 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 | Milímetros | 2023 | [PUB] |
| FedVQA: Personalized Federated Visual Question Answering over Heterogeneous Scenes | Leiden University | Milímetros | 2023 | [PUB] |
| Towards Fast and Stable Federated Learning: Confronting Heterogeneity via Knowledge Anchor | XJTU | Milímetros | 2023 | [PUB] [PDF] [CODE] |
| Federated Deep Multi-View Clustering with Global Self-Supervision | UESTC | Milímetros | 2023 | [PUB] [PDF] |
| FedAA: Using Non-sensitive Modalities to Improve Federated Learning while Preserving Image Privacy | ZJU | Milímetros | 2023 | [PUB] |
| Prototype-guided Knowledge Transfer for Federated Unsupervised Cross-modal Hashing | SDNU | Milímetros | 2023 | [PUB] [CODE] |
| Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data | ZJU | Milímetros | 2023 | [PUB] [PDF] |
| FedCD: A Classifier Debiased Federated Learning Framework for Non-IID Data | BUPT | Milímetros | 2023 | [PUB] |
| Federated Learning with Label-Masking Distillation | UCAS | Milímetros | 2023 | [PUB] [CODE] |
| Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data | SDU | Milímetros | 2023 | [PUB] [PDF] [CODE] |
| A Four-Pronged Defense Against Byzantine Attacks in Federated Learning | HUST | Milímetros | 2023 | [PUB] [PDF] |
| Client-Adaptive Cross-Model Reconstruction Network for Modality-Incomplete Multimodal Federated Learning | CAS; Peng Cheng Laboratory; UCAS | Milímetros | 2023 | [PUB] |
| FedGH: Heterogeneous Federated Learning with Generalized Global Header | NKU | Milímetros | 2023 | [PUB] [PDF] [CODE] |
| Cuing Without Sharing: A Federated Cued Speech Recognition Framework via Mutual Knowledge Distillation | CUHK | Milímetros | 2023 | [PUB] [PDF] [CODE] |
| AffectFAL: Federated Active Affective Computing with Non-IID Data | TJUT | Milímetros | 2023 | [PUB] [CODE] |
| Improving Federated Person Re-Identification through Feature-Aware Proximity and Aggregation | SZU | Milímetros | 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 | University of 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 | Duke University | ICCV | 2023 | [PUB] [PDF] [CODE] |
| Multi-Metrics Adaptively Identifies Backdoors in Federated Learning | SCUT | 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 | A Universidade de Sydney | 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 | QU | ICCV | 2023 | [PUB] [CODE] [SUPP] |
| Personalized Semantics Excitation for Federated Image Classification | Tulane University | 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 | QU | 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 | HUST | 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 | Meituan | 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 | UM | 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 | QUI | CVPR | 2023 | [PUB] [PDF] [CODE] |
| Confidence-Aware Personalized Federated Learning via Variational Expectation Maximization | KU Leuven | 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 | NTU | 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 | Meituan | 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 | Milímetros | 2022 | [PUB] |
| Few-Shot Model Agnostic Federated Learning | QU | Milímetros | 2022 | [PUB] [CODE] |
| Feeling Without Sharing: A Federated Video Emotion Recognition Framework Via Privacy-Agnostic Hybrid Aggregation | TJUT | Milímetros | 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 | BATER | 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 | Duke University | 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 | Nanjing University | 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 | Universidade de Wuhan | CVPR | 2022 | [PUB] [CODE] [VIDEO] |
| Robust Federated Learning With Noisy and Heterogeneous Clients | Universidade de Wuhan | CVPR | 2022 | [PUB] [SUPP] [CODE] |
| ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning | Arizona State University | 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; A Universidade de Sydney | 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; Pesquisa do Google | 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 | Duke University | 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 | Johns Hopkins University | CVPR | 2021 | [PUB] [PDF] [CODE] |
| Model-Contrastive Federated Learning | NUS; UC 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 | Duke University | 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 | NTU | Milímetros | 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 | Milímetros | 2020 | [PUB] | |
Performance Optimization of Federated Person Re-identification via Benchmark Analysis data. | NTU | Milímetros | 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 | Afiliação | Local | Ano | Materiais |
|---|---|---|---|---|
| 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 | Universidade de Auburn | 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 | OSSU | ACL | 2023 | [PUB] [PDF] [CODE] |
| FEDLEGAL: The First Real-World Federated Learning Benchmark for Legal NLP | BATER; 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 | University of Alberta | EMNLP | 2022 | [PUB] [PDF] [CODE] |
| Federated Model Decomposition with Private Vocabulary for Text Classification | BATER; 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 | Amazônia | 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; Amazônia | NAACL | 2022 | [PUB] [PDF] |
| Training Mixed-Domain Translation Models via Federated Learning | Amazônia | NAACL | 2022 | [PUB] [PAGE] [PDF] |
| Pretrained Models for Multilingual Federated Learning | Johns Hopkins University | NAACL | 2022 | [PUB] [PDF] [CODE] |
| Federated Chinese Word Segmentation with Global Character Associations | Universidade 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 | Universidade 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; Amazônia | 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 | Afiliação | Local | Ano | Materiais |
|---|---|---|---|---|
| ReFer: Retrieval-Enhanced Vertical Federated Recommendation for Full Set User Benefit | QUI | 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 | Universidade Técnica de Munique | 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 | Afiliação | Local | Ano | Materiais |
|---|---|---|---|---|
| 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 | Universidade de Columbia | ICDE | 2023 | [PUB] [CODE] |
| FedKNOW: Federated Continual Learning with Signature Task Knowledge Integration at Edge | PEDAÇO | ICDE | 2023 | [PUB] [PDF] |
| Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices | SJTU | ICDE | 2023 | [PUB] [PDF] |
| Federated IoT Interaction Vulnerability Analysis | MSU | 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. | PEDAÇO | 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. | NUS | 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. | NUS | 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. | BATER | 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. | NUS | 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 | NUS | 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 | Simon Fraser University | 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 | NUS | 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 | Afiliação | Local | Ano | Materiais |
|---|---|---|---|---|
| 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 | NTU | 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 | UW | 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 | NTU | 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. | Universidade 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 Leuven | WWW (Companion Volume) | 2023 | [PUB] |
| Privacy-Preserving Online Content Moderation: A Federated Learning Use Case. | CORTE | WWW (Companion Volume) | 2023 | [PUB] [PDF] |
| Privacy-Preserving Online Content Moderation with Federated Learning. | CORTE | 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 | QUI | INFOCOM | 2023 | [PUB] |
| A Reinforcement Learning Approach for Minimizing Job Completion Time in Clustered Federated Learning | Southeast University | 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 | HUST | INFOCOM | 2023 | [PUB] |
| Asynchronous Federated Unlearning | University of Toronto | INFOCOM | 2023 | [PUB] [PDF] [CODE] |
| Communication-Efficient Federated Learning for Heterogeneous Edge Devices Based on Adaptive Gradient Quantization | PSU | 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 | A Universidade de Sydney | INFOCOM | 2023 | [PUB] [PDF] |
| FedMoS: Taming Client Drift in Federated Learning with Double Momentum and Adaptive Selection | HUST | INFOCOM | 2023 | [PUB] [PDF] [CODE] |
| FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning | NTU | 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 | Northwestern University | INFOCOM | 2023 | [PUB] |
| More than Enough is Too Much: Adaptive Defenses against Gradient Leakage in Production Federated Learning | University of 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 | Universidade de Auburn | 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 | MSU | 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 | Korea University | INFOCOM | 2022 | [PUB] |
| Towards Optimal Multi-Modal Federated Learning on Non-IID Data with Hierarchical Gradient Blending | University of 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 | NEU | INFOCOM | 2022 | [PUB] [CODE] |
| A Profit-Maximizing Model Marketplace with Differentially Private Federated Learning | CUHK; AIRS | 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 | Yonsei University | 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 | University of California | SIGMETRICS | 2021 | [PUB] [PDF] |
| Hermes: an efficient federated learning framework for heterogeneous mobile clients | Duke University | 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. | Nanjing University | 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 | QUI | 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; Yale University | 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 | Arizona State University | 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. | University of 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. | Nanjing University | INFOCOM | 2020 | [PUB] |
| Optimizing Federated Learning on Non-IID Data with Reinforcement Learning | University of Toronto | INFOCOM | 2020 | [PUB] [SLIDE] [CODE] [解读] |
| Enabling Execution Assurance of Federated Learning at Untrusted Participants | QUI | 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 | A Universidade de Sydney | INFOCOM | 2019 | [PUB] [CODE] |
| Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning | Universidade de Wuhan | 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 | Afiliação | Local | Ano | Materiais |
|---|---|---|---|---|
| AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems. | DAC | 2024 | [PUB] | |
| Fake Node-Based Perception Poisoning Attacks against Federated Object Detection Learning in Mobile Computing Networks | DAC | 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 | Korea University | 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 | Virgínia Tecnologia | 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 | Universidade de Santo André | TPDS | 2024 | [PUB] [PDF] [CODE] |
| FedHAP: Federated Hashing With Global Prototypes for Cross-Silo Retrieval | QUI | 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 | BATER | 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 | HUST | 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 | UC 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 | Universidade de 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 | Anhui University | 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 | BATER | TPDS | 2023 | [PUB] [PDF] |
| Personalized Edge Intelligence via Federated Self-Knowledge Distillation. | HUST | TPDS | 2023 | [PUB] [CODE] |
| Design of a Quantization-Based DNN Delta Compression Framework for Model Snapshots and Federated Learning. | BATER | 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 | Universidade da Califórnia em San Diego | DAC | 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 | QUI | 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 | PEDAÇO | TPDS | 2022 | [PUB] |
| Federated Learning With Nesterov Accelerated Gradient | A Universidade de Sydney | 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. | QUI | TPDS | 2022 | [PUB] |
| DONE: Distributed Approximate Newton-type Method for Federated Edge Learning. | Universidade de 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. | NTU | 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. | SCU | 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 | University of 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 | DAC | 2021 | [PDF] [PUB] |
| Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration. | GMU | DAC | 2021 | [PDF] [PUB] |
| FedLight: Federated Reinforcement Learning for Autonomous Multi-Intersection Traffic Signal Control. | ECNU | DAC | 2021 | [PUB] |
| Oort: Efficient Federated Learning via Guided Participant Selection | University of 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 | SCUT | 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. | University of 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 | University of 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. | NUS | 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 | Afiliação | Local | Ano | Materiais |
|---|---|---|---|---|
| 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. | Virgínia Tecnologia | 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 | Afiliação | Local | Ano | Materiais |
|---|---|---|---|---|
| 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. Computação. 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. Computação. 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. Sist. | 2023 | [PUB] [PDF] |
| Short-Term Traffic Flow Prediction Based on Graph Convolutional Networks and Federated Learning | ZUEL | IEEE Trans. Intell. Transp. Sist. | 2023 | [PUB] |
| Hyper-Graph Attention Based Federated Learning Methods for Use in Mental Health Detection. | HVL | IEEE J. Biomed. Health Informatics | 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. Computação. Soc. Sist. | 2023 | [PUB] [PDF] [CODE] |
| ESA-FedGNN: Efficient secure aggregation for federated graph neural networks. | Peer Peer Netw. Appl. | 2023 | [PUB] | |
| FedCKE: Cross-Domain Knowledge Graph Embedding in Federated Learning | SWJTU | IEEE Trans. Grandes dados | 2023 | [PUB] |
| Asynchronous federated learning with directed acyclic graph-based blockchain in edge computing: Overview, design, and challenges. | Expert Syst. Appl. | 2023 | [PUB] | |
| FedGR: Federated Graph Neural Network for Recommendation System | CUPT | Axioms | 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 | Appl. 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. Technol. | 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. | University of 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 | QUI | Nature Communications | 2022 | [PUB] [CODE] [解读] |
| Malicious Transaction Identification in Digital Currency via Federated Graph Deep Learning | PEDAÇO | 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 dados | 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 | Interno. J. Bio Inspired |
