Awesome FL
v2024-beta
Table des matières
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Avis de mise à jour du référentiel
2024/09/30
Chers utilisateurs, Nous souhaitons vous informer de quelques changements qui affecteront ce référentiel open source. Le propriétaire et principal contributeur @youngfish42 a terminé avec succès ses études doctorales ? au 30 septembre 2024, et a depuis réorienté ses recherches. Ce changement de circonstances aura un impact sur la fréquence et l'étendue des mises à jour de la liste papier du référentiel.
Au lieu des mises à jour régulières précédentes, nous prévoyons que la liste papier sera désormais mise à jour sur une base mensuelle ou trimestrielle. De plus, la profondeur de ces mises à jour sera réduite. Par exemple, les mises à jour liées à l'institution de l'auteur et au code open source ne seront plus activement maintenues.
Nous comprenons que cela pourrait affecter la valeur que vous tirez de ce référentiel. Par conséquent, nous invitons humblement davantage de contributeurs à participer à la mise à jour du contenu. Cet effort de collaboration garantira que le référentiel reste une ressource précieuse pour tous.
Nous apprécions votre compréhension et comptons sur votre soutien et vos contributions continus.
Cordialement,
白小鱼 (jeune poisson)
catégories
Intelligence artificielle (IJCAI, AAAI, AISTATS, ALT, AI)
Apprentissage automatique (NeurIPS, ICML, ICLR, COLT, UAI, Apprentissage automatique, JMLR, TPAMI)
Exploration de données (KDD, WSDM)
Sécurisé (S&P, CCS, USENIX Security, NDSS)
Vision par ordinateur (ICCV, CVPR, ECCV, MM, IJCV)
Traitement du langage naturel (ACL, EMNLP, NAACL, COLING)
Recherche d'informations (SIGIR)
Base de données (SIGMOD, ICDE, VLDB)
Réseau (SIGCOMM, INFOCOM, MOBICOM, NSDI, WWW)
Système (OSDI, SOSP, ISCA, MLSys, EuroSys, TPDS, DAC, TOCS, TOS, TCAD, TC)
Autres (ICSE, FOCS, STOC)
| Lieu | 2024-2020 | avant 2020 |
|---|---|---|
| IJCAI | 24, 23, 22, 21, 20 | 19 |
| AAAI | 24, 23, 22, 21, 20 | - |
| AISTATS | 24, 23, 22, 21, 20 | - |
| ALT | 22 | - |
| IA (J) | 23 | - |
| NeuroIPS | 24, 23, 22, 21, 20 | 18, 17 |
| CIML | 24, 23, 22, 21, 20 | 19 |
| ICLR | 24, 23, 22, 21, 20 | - |
| POULAIN | 23 | - |
| AUI | 23, 22, 21 | - |
| Apprentissage automatique (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 |
| CSC | 24, 23, 22, 21, 19 | 17 |
| Sécurité USENIX | 23, 22, 20 | - |
| SNSD | 24, 23, 22, 21 | - |
| CVPR | 24, 23, 22, 21 | - |
| ICCV | 23,21 | - |
| ECVC | 24, 22, 20 | - |
| MM | 24, 23, 22, 21, 20 | - |
| IJCV (J) | 24 | - |
| Liste de contrôle d'accès | 23, 22, 21 | 19 |
| NAACL | 24, 22, 21 | - |
| EMNLP | 24, 23, 22, 21, 20 | - |
| COLAGE | 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 | |
| INDS | 23(1, 2) | - |
| WWW | 24, 23, 22, 21 | |
| OSDI | 21 | - |
| SOSP | 21 | - |
| ISCA | 24 | - |
| MLSys | 24, 23, 22, 20 | 19 |
| EuroSys | 24, 23, 22, 21, 20 | |
| TPDS (J) | 24, 23, 22, 21, 20 | - |
| CAD | 24, 22, 21 | - |
| COT | - | - |
| Conditions d'utilisation | - | - |
| TCAD | 24, 23, 22, 21 | - |
| CT | 24, 23, 22, 21 | - |
| ICSE | 23, 21 | - |
| FOCS | - | - |
| STOC | - | - |
mots-clés
Statistiques : le code est disponible & étoiles >= 100 | citation >= 50 | ? Lieu de premier plan
kg. : Graphique des connaissances | data. : ensemble de données | surv. : enquête
Les articles d'apprentissage fédéré dans Nature (et ses sous-revues), Cell, Science (et Science Advances) et PANS font référence au moteur de recherche WOS.
| Titre | Affiliation | Lieu | Année | Matériels |
|---|---|---|---|---|
| MatSwarm : calcul de matériaux fiable basé sur l'apprentissage par transfert en essaim pour un partage sécurisé de Big Data | USTB ; NTU | Nat. Commun. | 2024 | [PUB] [CODE] |
| Introduction de l'intelligence de pointe aux compteurs intelligents via l'apprentissage fractionné fédéré | HKU | Nat. Commun. | 2024 | [PUB] [新闻] |
| Une étude internationale présentant une plateforme d’apprentissage fédérée d’IA pour les tumeurs cérébrales pédiatriques | Université de Stanford | Nat. Commun. | 2024 | [PUB] [CODE] |
| PPML-Omics : une méthode d'apprentissage automatique fédéré préservant la confidentialité protège la confidentialité des patients dans les données omiques | KAUST | Avancées scientifiques | 2024 | [PUB] [CODE] |
| L’apprentissage fédéré n’est pas une panacée pour l’éthique des données | TUM ; UVA | Nat. Mach. Intell. (Commentaire) | 2024 | [PUB] |
| Modèle d'apprentissage fédéré robuste pour identifier les patients à haut risque présentant une récidive postopératoire d'un cancer gastrique | Hôpital central de Jiangmen ; Université de technologie aérospatiale de Guilin ; Université de technologie électronique de Guilin ; | Nat. Commun. | 2024 | [PUB] [CODE] |
| Partage sélectif des connaissances pour une distillation fédérée préservant la vie privée sans bon professeur | HKUST | Nat. Commun. | 2024 | [PUB] [PDF] [CODE] |
| Un système d'apprentissage fédéré pour l'oncologie de précision en Europe : DigiONE | IQVIA Cancer Research B.V. | Nat. Méd. (Commentaire) | 2024 | [PUB] |
| Calcul quantique aveugle distribué multi-clients avec l'architecture Qline | Université Sapienza de Rome | Nat. Commun. | 2023 | [PUB] [PDF] |
| Caractère aléatoire quantique indépendant de l’appareil – preuve améliorée de connaissance nulle | USTC | PNAS | 2023 | [PUB] [PDF] [新闻] |
| Tri collaboratif et respectueux de la confidentialité des batteries hors d'usage pour un recyclage direct et rentable via l'apprentissage automatique fédéré | Université Qinghua | Nat. Commun. | 2023 | [PUB] |
| Plaidoyer pour la confidentialité des données neurologiques et la réglementation des neurotechnologies | Université de Colombie | Nat. Protocole. (Perspective) | 2023 | [PUB] |
| Benchmark fédéré de l’intelligence artificielle médicale avec MedPerf | IHU Strasbourg ; Université de Strasbourg ; Institut du cancer Dana-Farber ; Médecine Weill Cornell ; École de santé publique TH Chan de Harvard ; MIT ; Intel | Nat. Mach. Intell. | 2023 | [PUB] [PDF] [CODE] |
| Équité algorithmique dans l’intelligence artificielle pour la médecine et les soins de santé | École de médecine de Harvard ; Broad Institute de Harvard et Massachusetts Institute of Technology ; Institut du cancer Dana-Farber | Nat. Bioméde. Ing. (Perspective) | 2023 | [PUB] [PDF] |
| Transfert de connaissances différentiellement privé pour l’apprentissage fédéré | JEU | Nat. Commun. | 2023 | [PUB] [CODE] |
| Apprentissage fédéré décentralisé grâce au partage de modèles proxy | IA de couche 6 ; Université de Waterloo ; Institut du vecteur | Nat. Commun. | 2023 | [PUB] [PDF] [CODE] |
| Apprentissage automatique fédéré dans la recherche conforme à la protection des données | Université de Hambourg | Nat. Mach. Intell. (Commentaire) | 2023 | [PUB] |
| Apprentissage fédéré pour prédire la réponse histologique à la chimiothérapie néoadjuvante dans le cancer du sein triple négatif | Owkin | Nat. Méd. | 2023 | [PUB] [CODE] |
| L'apprentissage fédéré permet le Big Data pour la détection des limites des cancers rares | Université de Pennsylvanie | Nat. Commun. | 2022 | [PUB] [PDF] [CODE] |
| Apprentissage fédéré et souveraineté des données génomiques autochtones | Visage câlin | Nat. Mach. Intell. (Commentaire) | 2022 | [PUB] |
| Apprentissage de représentations fédérées et démêlées pour la détection non supervisée d'anomalies cérébrales | TUM | Nat. Mach. Intell. | 2022 | [PUB] [PDF] [CODE] |
| Faire passer l'apprentissage automatique pour les soins de santé du développement au déploiement et des modèles aux données | l'Université de Stanford ; Biosciences de pierre verte | Nat. Bioméde. Ing. (Article de révision) | 2022 | [PUB] |
| Un cadre de réseau neuronal graphique fédéré pour une personnalisation préservant la confidentialité | JEU | Nat. Commun. | 2022 | [PUB] [CODE] [解读] |
| Apprentissage fédéré efficace en matière de communication via la distillation des connaissances | JEU | Nat. Commun. | 2022 | [PUB] [PDF] [CODE] |
| Diriger l'apprentissage neuromorphique fédéré pour l'intelligence artificielle de pointe sans fil | XMU ; NTU | Nat. Commun. | 2022 | [PUB] [CODE] [解读] |
| Une nouvelle approche d'apprentissage fédéré décentralisé pour se former sur des données médicales privées distribuées à l'échelle mondiale, de mauvaise qualité et protégées. | Université de Wollongong | Sci. représentant | 2022 | [PUB] |
| Faire progresser le diagnostic du COVID-19 grâce à une collaboration respectueuse de la vie privée dans le domaine de l'intelligence artificielle | HUST | Nat. Mach. Intell. | 2021 | [PUB] [PDF] [CODE] |
| Apprentissage fédéré pour prédire les résultats cliniques chez les patients atteints de COVID-19 | Radiologie MGH et Harvard Medical School | Nat. Méd. | 2021 | [PUB] [CODE] |
| Interférence contradictoire et ses atténuations dans l'apprentissage automatique collaboratif préservant la confidentialité | Collège Impérial de Londres ; TUM ; OuvertMiné | Nat. Mach. Intell. (Perspective) | 2021 | [PUB] |
| Swarm Learning pour un apprentissage automatique clinique décentralisé et confidentiel | DZNE ; Université de Bonn ; | Nature ? | 2021 | [PUB] [CODE] [LOGICIEL] [解读] |
| Confidentialité de bout en bout préservant l’apprentissage profond sur l’imagerie médicale multi-institutionnelle | TUM ; Collège Impérial de Londres ; OuvertMiné | Nat. Mach. Intell. | 2021 | [PUB] [CODE] [解读] |
| Apprentissage fédéré efficace en matière de communication | CUHK ; Université de Princeton | POÊLES. | 2021 | [PUB] [CODE] |
| Briser les frontières du partage de données médicales grâce à l'utilisation de radiographies synthétisées | Université RWTH d'Aix-la-Chapelle | Science. Avances. | 2020 | [PUB] [CODE] |
| Apprentissage automatique sécurisé, préservant la confidentialité et fédéré en imagerie médicale | TUM ; Collège Impérial de Londres ; OuvertMiné | Nat. Mach. Intell. (Perspective) | 2020 | [PUB] |
Articles d'apprentissage fédéré acceptés par les principales conférences et revues sur l'IA (intelligence artificielle), notamment IJCAI (International Joint Conference on Artificial Intelligence), AAAI (AAAI Conference on Artificial Intelligence), AISTATS (Artificial Intelligence and Statistics), ALT (International Conference on Algorithmic Learning). Théorie), IA(Intelligence Artificielle).
| Titre | Affiliation | Lieu | Année | Matériels |
|---|---|---|---|---|
| Clustering multi-vues fédéré via la factorisation tensorielle | IJCAI | 2024 | [PUB] | |
| Clustering multi-vues fédéré efficace avec factorisation matricielle intégrée et K-Means | IJCAI | 2024 | [PUB] | |
| LG-FGAD : un cadre de détection d'anomalies de graphes fédérés efficace | IJCAI | 2024 | [PUB] | |
| Apprentissage rapide fédéré pour les modèles Weather Foundation sur les appareils | IJCAI | 2024 | [PUB] | |
| Briser les barrières de l'hétérogénéité des systèmes : apprentissage fédéré multimodal tolérant les retardataires via la distillation des connaissances | IJCAI | 2024 | [PUB] | |
| Désapprendre pendant l'apprentissage : une méthode de désapprentissage automatique fédérée efficace | IJCAI | 2024 | [PUB] | |
| Compression de gradient hybride pratique pour les systèmes d'apprentissage fédéré | IJCAI | 2024 | [PUB] | |
| Découverte causale fédérée tenant compte de l'hétérogénéité de la qualité des échantillons grâce à la sélection adaptative d'espace variable | IJCAI | 2024 | [PUB] [CODE] | |
| Apprentissage fédéré régularisé par les normes de fonctionnalités : utiliser les disparités de données pour améliorer les performances des modèles | IJCAI | 2024 | [PUB] [CODE] | |
| Quantification de l'incertitude basée sur Dirichlet pour un apprentissage fédéré personnalisé avec des réseaux postérieurs améliorés | IJCAI | 2024 | [PUB] | |
| FedConPE : des bandits conversationnels fédérés efficaces avec des clients hétérogènes | IJCAI | 2024 | [PUB] | |
| DarkFed : une attaque dérobée sans données dans l'apprentissage fédéré | IJCAI | 2024 | [PUB] | |
| Désapprentissage fédéré évolutif via un partage isolé et codé | IJCAI | 2024 | [PUB] | |
| Amélioration de la recommandation inter-domaines à double cible grâce à un apprentissage fédéré préservant la confidentialité | IJCAI | 2024 | [PUB] | |
| Fuite d'étiquettes dans l'apprentissage fédéré vertical : une enquête | IJCAI | 2024 | [PUB] | |
| L’essor de l’intelligence fédérée : des modèles de fondations fédérées vers l’intelligence collective | IJCAI | 2024 | [PUB] | |
| LEAP : Optimisation de l'apprentissage fédéré hiérarchique sur les données non-IID avec le jeu de formation de coalition | IJCAI | 2024 | [PUB] | |
| EAB-FL : exacerber les biais algorithmiques grâce à des attaques d'empoisonnement de modèles dans l'apprentissage fédéré | IJCAI | 2024 | [PUB] | |
| Distillation des connaissances dans l'apprentissage fédéré : un guide pratique | IJCAI | 2024 | [PUB] | |
| FedGCS : un cadre génératif pour une sélection efficace des clients dans l'apprentissage fédéré via une optimisation basée sur les gradients | IJCAI | 2024 | [PUB] | |
| FedPFT : mise au point par proxy fédéré des modèles de fondation | IJCAI | 2024 | [PUB] [CODE] | |
| Une enquête systématique sur l'apprentissage fédéré semi-supervisé | IJCAI | 2024 | [PUB] | |
| Agents intelligents pour l'apprentissage fédéré basé sur les enchères : une enquête | IJCAI | 2024 | [PUB] | |
| Une stratégie d'enchères sans biais et maximisant les revenus pour les consommateurs de données dans l'apprentissage fédéré basé sur les enchères | IJCAI | 2024 | [PUB] | |
| Apprentissage fédéré personnalisé basé sur un double calibrage | IJCAI | 2024 | [PUB] | |
| Aide à la décision orientée parties prenantes pour l'apprentissage fédéré basé sur les enchères | IJCAI | 2024 | [PUB] | |
| Redéfinir les contributions : apprentissage fédéré piloté par Shapley | IJCAI | 2024 | [PUB] [CODE] | |
| Une enquête sur les méthodes d'apprentissage fédéré efficaces pour la formation sur les modèles de base | IJCAI | 2024 | [PUB] | |
| De l'optimisation à la généralisation : apprentissage fédéré équitable contre changement de qualité via l'adéquation de la netteté entre clients | IJCAI | 2024 | [PUB] [CODE] | |
| FBLG : une approche basée sur des graphiques locaux pour gérer les données non-IID à double asymétrie dans l'apprentissage fédéré | IJCAI | 2024 | [PUB] | |
| FedFa : un paradigme de formation entièrement asynchrone pour l'apprentissage fédéré | IJCAI | 2024 | [PUB] | |
| FedSSA : agrégation basée sur la similarité sémantique pour un apprentissage fédéré personnalisé efficace et hétérogène | IJCAI | 2024 | [PUB] | |
| FedES : arrêt anticipé fédéré pour empêcher la mémorisation du bruit d'étiquette hétérogène | IJCAI | 2024 | [PUB] | |
| Apprentissage fédéré personnalisé pour la prévision du trafic interurbain | IJCAI | 2024 | [PUB] | |
| Adaptation fédérée pour les recommandations basées sur le modèle de base | IJCAI | 2024 | [PUB] | |
| BADFSS : attaques dérobées contre l'apprentissage fédéré et autosupervisé | IJCAI | 2024 | [PUB] | |
| Estimer avant de débiaiser : une approche bayésienne pour détacher les biais antérieurs dans l'apprentissage fédéré semi-supervisé | IJCAI | 2024 | [PUB] [CODE] | |
| FedTAD : distillation de connaissances sans données et tenant compte de la topologie pour l'apprentissage fédéré par sous-graphes | IJCAI | 2024 | [PUB] | |
| BOBA : apprentissage fédéré robuste et byzantin avec asymétrie des étiquettes | UUIUC | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| Bandits contextuels linéaires fédérés avec des clients hétérogènes | Université de Virginie | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| Conception d'expériences fédérées sous confidentialité différentielle distribuée | l'Université de Stanford ; Méta | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| Échapper aux points de selle dans l'apprentissage fédéré hétérogène via SGD distribué avec compression de communication | Université de Princeton | AISTATS | 2024 | [PUB] [PDF] |
| SGD asynchrone sur graphiques : un cadre unifié pour l'optimisation asynchrone, décentralisée et fédérée | INRIA | AISTATS | 2024 | [PUB] [PDF] |
| SIFU : désapprentissage fédéré séquentiel et informé pour un désapprentissage client efficace et prouvable dans l'optimisation fédérée | INRIA | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| Compression avec distribution exacte des erreurs pour l'apprentissage fédéré | École Polytechnique | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| Optimisation Minimax fédérée adaptative avec des complexités moindres | NJU ; Laboratoire clé du MIIT d'analyse de modèles et d'intelligence artificielle | AISTATS | 2024 | [PUB] [PDF] |
| Compression adaptative dans l'apprentissage fédéré via des informations secondaires | l'Université de Stanford ; Université de Padoue | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| Apprentissage fédéré à la demande pour des distributions de classes cibles arbitraires | UNISTE | AISTATS | 2024 | [PUB] [CODE] |
| FedFisher : exploiter les informations de Fisher pour un apprentissage fédéré ponctuel | CMU | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| Dynamique de file d'attente de l'apprentissage fédéré asynchrone | Huawei | AISTATS | 2024 | [PUB] [PDF] |
| Bandit armé X fédéré personnalisé | Université Purdue | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| Apprentissage fédéré pour les dossiers de santé électroniques hétérogènes utilisant des réseaux d'attention à graphiques temporels augmentés | Université d'Oxford | AISTATS | 2024 | [PUB] [CODE] |
| Ascension de descente de gradient lissée stochastique pour une optimisation Minimax fédérée | Université de Virginie | AISTATS | 2024 | [PUB] [PDF] |
| Comprendre la généralisation de l'apprentissage fédéré via la stabilité : l'hétérogénéité compte | Université du Nord-Ouest | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| Avantages mutuels prouvables de l’apprentissage fédéré dans les domaines sensibles à la confidentialité | Université de Sofia | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| Analyse des fuites de confidentialité dans les grands modèles de langage fédérés | Université de Floride | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| Agrégateur invariant pour se défendre contre les attaques de portes dérobées fédérées | UUIUC | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| Apprentissage fédéré efficace en matière de communication avec hétérogénéité des données et des clients | ISTA | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| FedMut : apprentissage fédéré généralisé via mutation stochastique | NTU | AAAI | 2024 | [PUB] |
| Apprentissage partiel fédéré d'étiquettes avec augmentation et régularisation adaptatives locales | Université Carleton | AAAI | 2024 | [PUB] [PAGE] |
| Aucun préjugé ! Réseaux de neurones à graphiques fédérés équitables pour des recommandations personnalisées | ITI | AAAI | 2024 | [PUB] [PAGE] [PDF] [CODE] |
| La logique formelle a permis un apprentissage fédéré personnalisé grâce à l'inférence de propriété | Université Vanderbilt | AAAI | 2024 | [PUB] [PDF] |
| Apprentissage de représentation indépendant des tâches et préservant la confidentialité pour un apprentissage fédéré contre les attaques par inférence d'attributs | Technologie de l'Illinois | AAAI | 2024 | [PUB] [PAGE] [PDF] [CODE] |
| FairTrade : parvenir à des compromis Pareto optimaux entre précision équilibrée et équité dans l'apprentissage fédéré | Université Leibniz | AAAI | 2024 | [PUB] [PAGE] |
| Combattre les déséquilibres de données dans l'apprentissage fédéré semi-supervisé avec des régulateurs doubles | HKUST | AAAI | 2024 | [PUB] [PAGE] [PDF] |
| Fed-QSSL : un cadre pour l'apprentissage fédéré personnalisé dans des conditions de largeur de bit et d'hétérogénéité des données | Utah | AAAI | 2024 | [PUB] [PAGE] [PDF] |
| Sur le démêlage du transfert asymétrique de connaissances pour l'apprentissage fédéré indépendant des modalités et des tâches | Université de Virginie | AAAI | 2024 | [PUB] |
| FedDAT : une approche pour la mise au point du modèle de base dans l'apprentissage fédéré hétérogène multimodal | LMU Munich Siemens AG | AAAI | 2024 | [PUB] [PAGE] [PDF] [CODE] |
| Surveillez votre tête : Assemblage de têtes de projection pour préserver la fiabilité des modèles fédérés | Laboratoire clé commun pour l'intelligence artificielle de l'Université Jiaotong de Xi'an Shaanxi | AAAI | 2024 | [PUB] [PAGE] [PDF] |
| FedGCR : Atteindre la performance et l'équité pour l'apprentissage fédéré avec des types de clients distincts via la personnalisation et la repondération des groupes | NTU | AAAI | 2024 | [PUB] [PAGE] [CODE] |
| Encodeurs fédérés spécifiques aux modalités et ancres multimodales pour la segmentation personnalisée des tumeurs cérébrales | Université de Xiamen | AAAI | 2024 | [PUB] [PAGE] [PDF] [CODE] |
| Exploiter les biais d'étiquettes dans l'apprentissage fédéré avec la concaténation de modèles | NOUS | AAAI | 2024 | [PUB] [PAGE] [PDF] [CODE] |
| Distillation de connaissances complémentaires pour un modèle robuste et préservant la confidentialité servant dans l'apprentissage fédéré vertical | SUST; HKUST | AAAI | 2024 | [PUB] [PAGE] |
| Apprentissage fédéré via la distillation collaborative entrées-sorties | Université de Buffalo ; École de médecine de Harvard aux États-Unis | AAAI | 2024 | [PUB] [PAGE] [PDF] [CODE] |
| Apprentissage fédéré en un tour calibré avec inférence bayésienne dans l'espace prédictif | Institut vectoriel de l’Université de Waterloo | AAAI | 2024 | [PUB] [PAGE] [PDF] [CODE] |
| FedCSL : une approche évolutive et précise de l'apprentissage de la structure causale fédérée | HFUT | AAAI | 2024 | [PUB] [PDF] |
| FedFixer : Atténuer le bruit des étiquettes hétérogènes dans l'apprentissage fédéré | Université Jiaotong de Xi'an ; Université de Leyde | AAAI | 2024 | [PUB] [PAGE] [PDF] |
| FedLPS : apprentissage fédéré hétérogène pour plusieurs tâches avec partage de paramètres locaux | NJU | AAAI | 2024 | [PUB] [PAGE] [PDF] [CODE] |
| Apprentissage fédéré à trois niveaux prouvé et convergent | TJU | AAAI | 2024 | [PUB] [PDF] |
| Apprentissage fédéré performatif : une solution aux changements de distribution dépendants du modèle et hétérogènes | MU | AAAI | 2024 | [PUB] [PAGE] |
| Intelligence commerciale générale : moteur basé sur le NLP fédéré glocalement pour des services personnalisés durables et préservant la confidentialité des multi-commerçants | Université Kyung Hee ; Harex InfoTech | AAAI | 2024 | [PUB] [PAGE] |
| EMGAN : Early-Mix-GAN sur l'extraction de modèles côté serveur dans l'apprentissage fédéré fractionné | Sony IA | AAAI | 2024 | [PUB] [PAGE] [CODE] |
| FedDiv : Filtrage collaboratif du bruit pour l'apprentissage fédéré avec des étiquettes bruyantes | SYSU ; HKU | AAAI | 2024 | [PUB] [PAGE] [PDF] [CODE] |
| Transformateur de points avec apprentissage fédéré pour prédire le statut HER2 du cancer du sein à partir d'images de diapositives entières colorées à l'hématoxyline et à l'éosine | USTC ; CAS | AAAI | 2024 | [PUB] [PAGE] [PDF] [CODE] |
| FedNS : un algorithme de type Newton à esquisse rapide pour l'apprentissage fédéré | CAS | AAAI | 2024 | [PUB] [PDF] [CODE] |
| Bandit armé X fédéré | Université Purdue | AAAI | 2024 | [PUB] [PAGE] [PDF] [CODE] |
| Fondement algorithmique de l'apprentissage fédéré avec des données séquentielles | GMU | AAAI | 2024 | [PUB] |
| UFDA : Adaptation universelle des domaines fédérés avec des hypothèses pratiques | XJTU ; Université de Sydney | AAAI | 2024 | [PUB] [PAGE] [PDF] [CODE] |
| FedASMU : apprentissage fédéré asynchrone efficace avec mise à jour dynamique du modèle prenant en compte l'obsolescence | Hithink RoyalFlush Information Network Co. | AAAI | 2024 | [PUB] [PAGE] [PDF] |
| Transformateur guidé par le langage pour la classification multi-étiquettes fédérée | NTU | AAAI | 2024 | [PUB] [PAGE] [PDF] [CODE] |
| FedCD : apprentissage fédéré semi-supervisé avec équilibre de sensibilisation en classe via des enseignants doubles | SZU | AAAI | 2024 | [PUB] [PAGE] [CODE] |
| Au-delà des menaces traditionnelles : une attaque dérobée persistante contre l'apprentissage fédéré | UHE | AAAI | 2024 | [PUB] [PAGE] [CODE] |
| Apprentissage fédéré avec des clients extrêmement bruyants via distillation négative | XMU | AAAI | 2024 | [PUB] [PAGE] [PDF] [CODE] |
| FedST : apprentissage par transfert de style fédéré pour la segmentation d'images non IID | USTB | AAAI | 2024 | [PUB] [PAGE] [学报] [CODE] |
| PPIDSG : un système de partage de distribution d'images préservant la confidentialité avec GAN dans l'apprentissage fédéré | USTC | AAAI | 2024 | [PUB] [PAGE] [PDF] [CODE] |
| Un cadre de jumeau numérique cognitif (CDT) basé sur l'apprentissage fédéré préservant la confidentialité (PPFL) pour les villes intelligentes | DCU | AAAI | 2024 | [PUB] |
| Un algorithme primal-double pour l'apprentissage fédéré hybride | Université du Nord-Ouest | AAAI | 2024 | [PUB] [PAGE] [PDF] |
| FedLF : apprentissage fédéré équitable par couches | CUHK ; Institut d'intelligence artificielle et de robotique pour la société de Shenzhen | AAAI | 2024 | [PUB] [PAGE] |
| Vers un apprentissage fédéré équitable via des graphiques via des mécanismes d'incitation | ZJU ; FDU | AAAI | 2024 | [PUB] [PAGE] [PDF] [CODE] |
| Vers la robustesse de l’apprentissage fédéré différentiellement privé | JEU | AAAI | 2024 | [PUB] [PAGE] |
| Résister aux attaques par portes dérobées dans l'apprentissage fédéré via des élections bidirectionnelles et une perspective individuelle | ZJU ; Huawei | AAAI | 2024 | [PUB] [PAGE] [PDF] [CODE] |
| Un nombre entier suffit : quand l'apprentissage fédéré vertical rencontre l'arrondi | ZJU ; Groupe de fourmis | AAAI | 2024 | [PUB] [PAGE] |
| Apprentissage fédéré guidé par CLIP sur l'hétérogénéité et les données à longue traîne | XMU | AAAI | 2024 | [PUB] [PAGE] [PDF] [CODE] |
| Réglage des invites adaptatives fédérées pour l'apprentissage collaboratif multi-domaines | FDU | AAAI | 2024 | [PUB] [PAGE] [PDF] [CODE] |
| Apprentissage fédéré équitable et multidimensionnel | SDU | AAAI | 2024 | [PUB] [PAGE] [PDF] |
| HiFi-Gas : mécanisme d'incitation à l'apprentissage fédéré hiérarchique, estimation améliorée de la consommation de gaz | Groupe ENN | AAAI | 2024 | [PUB] |
| Sur le rôle de Server Momentum dans l'apprentissage fédéré | Université de Virginie | AAAI | 2024 | [PUB] [PDF] |
| FedCompetitors : collaboration harmonieuse dans l'apprentissage fédéré avec des participants concurrents | BUPT | AAAI | 2024 | [PUB] [PAGE] [PDF] |
| z-SignFedAvg : une compression stochastique unifiée basée sur les signes pour l'apprentissage fédéré | CUHK ; Chine Institut de recherche sur le Big Data à Shenzhen | AAAI | 2024 | [PUB] [PAGE] [PDF] |
| Apprentissage fédéré asynchrone tenant compte de la disparité des données et de l'indisponibilité temporelle pour la maintenance prédictive des flottes de transport | Groupe Volkswagen | AAAI | 2024 | [PUB] [PAGE] |
| Apprentissage de graphes fédérés sous changement de domaine avec des prototypes généralisables | QUOI | AAAI | 2024 | [PUB] [PAGE] |
| TurboSVM-FL : stimuler l'apprentissage fédéré grâce à l'agrégation SVM pour les clients paresseux | Université technique de Munich | AAAI | 2024 | [PUB] [PAGE] [PDF] [CODE] |
| Minimisation des écarts de gradient collaboratifs multi-sources pour la généralisation de domaines fédérés | TJU | AAAI | 2024 | [PUB] [PDF] [CODE] |
| Dissimulation des échantillons sensibles contre les fuites de gradient dans l'apprentissage fédéré | Université Monash | AAAI | 2024 | [PUB] [PAGE] [PDF] [CODE] |
| FedA3I : agrégation soucieuse de la qualité des annotations pour la segmentation fédérée d'images médicales contre le bruit d'annotation hétérogène | HUST | AAAI | 2024 | [PUB] [PAGE] [PDF] [CODE] |
| Apprentissage de causalité fédéré avec optimisation adaptative explicable | SDU | AAAI | 2024 | [PUB] [PAGE] [PDF] |
| Bandits contextuels fédérés en cascade avec communication asynchrone et utilisateurs hétérogènes | USTC | AAAI | 2024 | [PUB] [PAGE] [PDF] |
| Explorer l'apprentissage fédéré semi-supervisé One-Shot avec des modèles de diffusion pré-entraînés | FDU | AAAI | 2024 | [PUB] [PDF] |
| Stylisation co-contrainte diversité-authenticité pour la généralisation de domaines fédérés dans la réidentification des personnes | XMU ; Université de Trente | AAAI | 2024 | [PUB] [PAGE] |
| PerFedRLNAS : recherche d'architecture neuronale fédérée personnalisée et unique | Université de Toronto | AAAI | 2024 | [PUB] [PAGE] |
| Apprentissage fédéré asynchrone efficace avec agrégation de dynamique prospective et correction fine | BUPT | AAAI | 2024 | [PUB] [PAGE] |
| Attaques contradictoires contre les algorithmes de débit adaptatif à apprentissage fédéré | HKU | AAAI | 2024 | [PUB] |
| FedTGP : prototypes globaux entraînables avec apprentissage contrastif amélioré par marge adaptative pour l'hétérogénéité des données et des modèles dans l'apprentissage fédéré | SJTU | AAAI | 2024 | [PUB] [PAGE] [PDF] [CODE] |
| LR-XFL : apprentissage fédéré explicable basé sur le raisonnement logique | NTU | AAAI | 2024 | [PUB] [PDF] [CODE] |
| Une approche de minimisation des pertes de Huber pour un apprentissage fédéré robuste et byzantin | Laboratoire du Zhejiang | AAAI | 2024 | [PUB] [PAGE] [PDF] |
| Coaching des paramètres axé sur les connaissances pour un apprentissage fédéré personnalisé | Université du Nord-Est | AAAI | 2024 | [PUB] [PAGE] |
| Apprentissage fédéré du bruit des étiquettes avec régularisation des produits de diversité locale | SJTU | AAAI | 2024 | [PUB] [PAGE] [SUPP] |
| Agrégation pondérée adaptée dans l'apprentissage fédéré (résumé d'étudiant) | UBC | AAAI | 2024 | [PUB] |
| Transfert de connaissances via un modèle compact dans l'apprentissage fédéré (résumé d'étudiant) | Université de Sydney | AAAI | 2024 | [PUB] [PAGE] |
| PICSR : Routeur multi-silos basé sur des prototypes pour l'apprentissage fédéré (résumé d'étudiant) | Laboratoire autonome de l'Université d'État de l'Ohio, CMU | AAAI | 2024 | [PUB] [PAGE] |
| Réseau de convolution graphique préservant la confidentialité pour la recommandation d'éléments fédérés | SZU | IA | 2023 | [PUB] |
| Gagnant-gagnant : un cadre fédéré préservant la confidentialité pour la recommandation inter-domaines à double cible | CAS ; UCA ; Technologie JD ; Recherche JD sur les villes intelligentes | AAAI | 2023 | [PUB] |
| Attaque non ciblée contre les systèmes de recommandation fédérés via l'intégration d'éléments toxiques et la défense | USTC ; Laboratoire clé d'État d'intelligence cognitive | AAAI | 2023 | [PUB] [PDF] [CODE] |
| Crowdsourcing fédéré boosté par des incitations | SDU | AAAI | 2023 | [PUB] [PDF] |
| Lutter contre l'hétérogénéité des données dans l'apprentissage fédéré avec des prototypes de classe | Université Lehigh | AAAI | 2023 | [PUB] [PDF] [CODE] |
| FairFed : Permettre l'équité de groupe dans l'apprentissage fédéré | USC | AAAI | 2023 | [PUB] [PDF] [解读] |
| Propagation de la robustesse fédérée : partage de la robustesse contradictoire dans l'apprentissage fédéré hétérogène | MSU | AAAI | 2023 | [PUB] |
| Sparsification des compléments : élagage de modèles à faible surcharge pour l'apprentissage fédéré | NJIT | AAAI | 2023 | [PUB] |
| Communication presque gratuite dans le cadre de l'identification fédérée du meilleur bras | NOUS | AAAI | 2023 | [PUB] [PDF] |
| Agrégation de modèles adaptatifs par couches pour un apprentissage fédéré évolutif | Université de Californie du Sud Université Inha | AAAI | 2023 | [PUB] [PDF] |
| Empoisonnement avec Cerberus : attaque dérobée furtive et collusoire contre l'apprentissage fédéré | BJTU | AAAI | 2023 | [PUB] |
| FedMDFG : apprentissage fédéré avec descente multi-dégradés et orientation équitable | CUHK ; L'Institut d'intelligence artificielle et de robotique pour la société de Shenzhen | AAAI | 2023 | [PUB] |
| Sécuriser l'agrégation sécurisée : atténuer les fuites de confidentialité à plusieurs niveaux dans l'apprentissage fédéré | USC | AAAI | 2023 | [PUB] [PDF] [VIDÉO] [CODE] |
| Apprentissage fédéré sur des graphiques non-IID via le partage de connaissances structurelles | UTS | AAAI | 2023 | [PUB] [PDF] [CODE] |
| Identification efficace des similarités de distribution dans l'apprentissage fédéré en cluster via les angles principaux entre les sous-espaces de données client | UCSD | AAAI | 2023 | [PUB] [PDF] [CODE] |
| FedABC : Cibler une concurrence équitable dans l'apprentissage fédéré personnalisé | QUOI ; Laboratoire Hubei Luojia ; Académie JD Explorer | AAAI | 2023 | [PUB] [PDF] |
| Au-delà de l'ADMM : un cadre d'apprentissage fédéré adaptatif unifié à variance client réduite | SUTD | AAAI | 2023 | [PUB] [PDF] |
| FedGS : échantillonnage fédéré basé sur des graphiques avec disponibilité arbitraire des clients | XMU | AAAI | 2023 | [PUB] [PDF] [CODE] |
| Apprentissage fédéré adaptatif plus rapide | Université de Pittsburgh | AAAI | 2023 | [PUB] [PDF] |
| FedNP : vers un apprentissage fédéré non-IID via la propagation neuronale fédérée | HKUST | AAAI | 2023 | [PUB] [CODE] [VIDÉO] [SUPP] |
| Correspondance neuronale fédérée bayésienne qui complète les informations | TJU | AAAI | 2023 | [PUB] [PDF] |
| CDMA : un algorithme d'apprentissage fédéré multi-appareils pratique pour les problèmes généraux de Minimax | ZJU | AAAI | 2023 | [PUB] [PDF] [CODE] |
| Modèle génératif fédéré sur les données hétérogènes multi-sources dans l'IoT | SSG | AAAI | 2023 | [PUB] |
| DeFL : Se défendre contre les attaques d'empoisonnement de modèles dans l'apprentissage fédéré via la sensibilisation aux périodes d'apprentissage critiques | Université SUNY-Binghamton | AAAI | 2023 | [PUB] |
| FedALA : agrégation locale adaptative pour un apprentissage fédéré personnalisé | SJTU | AAAI | 2023 | [PUB] [PDF] [CODE] |
| Plonger dans la robustesse contradictoire de l’apprentissage fédéré | ZJU | AAAI | 2023 | [PUB] [PDF] |
| Sur la vulnérabilité des défenses dérobées pour l'apprentissage fédéré | TJU | AAAI | 2023 | [PUB] [PDF] [CODE] |
| Echo of Neighbours : amplification de la confidentialité pour un apprentissage fédéré privé personnalisé avec le modèle Shuffle | RUC; Centre de recherche en ingénierie du ministère de l'Éducation sur les bases de données et la BI | AAAI | 2023 | [PUB] [PDF] |
| DPAUC : calcul AUC différentiellement privé dans l'apprentissage fédéré | ByteDance Inc. | Pistes spéciales AAAI | 2023 | [PUB] [PDF] [CODE] |
| Formation efficace de modèles de diagnostic de pannes industrielles à grande échelle grâce à l'abandon de blocs opportunistes fédérés | NTU | Programmes spéciaux AAAI | 2023 | [PUB] [PDF] |
| Apprentissage fédéré orchestré à l’échelle de l’industrie pour la découverte de médicaments | KU Louvain | Programmes spéciaux AAAI | 2023 | [PUB] [PDF] [VIDÉO] |
| Un outil de surveillance de l'apprentissage fédéré pour la simulation de voitures autonomes (résumé d'étudiant) | CNU | Programmes spéciaux AAAI | 2023 | [PUB] |
| MGIA : Attaque d'inversion de gradient mutuel dans l'apprentissage fédéré multimodal (résumé d'étudiant) | PolyU | Programmes spéciaux AAAI | 2023 | [PUB] |
| Apprentissage fédéré en cluster pour les données hétérogènes (résumé d'étudiant) | RUC | Programmes spéciaux AAAI | 2023 | [PUB] |
| FedSampling : une meilleure stratégie d'échantillonnage pour l'apprentissage fédéré | JEU | IJCAI | 2023 | [PUB] [PDF] [CODE] |
| HyperFed : exploration de prototypes hyperboliques avec agrégation cohérente de données non IID dans l'apprentissage fédéré | ZJU | IJCAI | 2023 | [PUB] [PDF] |
| FedOBD : abandon de bloc opportuniste pour former efficacement des réseaux de neurones à grande échelle grâce à l'apprentissage fédéré | NTU | IJCAI | 2023 | [PUB] [PDF] [CODE] |
| Modélisation probabiliste fédérée de distribution des préférences avec co-clustering compact pour une recommandation multidomaine préservant la confidentialité | ZJU | IJCAI | 2023 | [PUB] |
| Apprentissage sémantique et structurel des graphes fédérés | QUOI | IJCAI | 2023 | [PUB] |
| BARA : mécanisme d'incitation efficace avec allocation de budget de récompense en ligne dans l'apprentissage fédéré inter-silos | SYSU | IJCAI | 2023 | [PUB] [PDF] |
| FedDWA : apprentissage fédéré personnalisé avec ajustement dynamique du poids | SYSU | IJCAI | 2023 | [PUB] [PDF] |
| FedPass : apprentissage profond fédéré vertical préservant la confidentialité avec obscurcissement adaptatif | Banque Web | IJCAI | 2023 | [PUB] [PDF] |
| Encodeur automatique de graphiques fédérés globalement cohérent pour les graphiques non IID | FZU | IJCAI | 2023 | [PUB] [CODE] |
| Apprentissage par renforcement multi-agents compétitif et coopératif pour l'apprentissage fédéré basé sur les enchères | NTU | IJCAI | 2023 | [PUB] |
| Double personnalisation sur recommandation fédérée | JLU ; Université de technologie de Sydney | IJCAI | 2023 | [PUB] [PDF] [CODE] |
| FedNoRo : Vers un apprentissage fédéré résistant au bruit en s'attaquant au déséquilibre des classes et à l'hétérogénéité du bruit des étiquettes | HUST | IJCAI | 2023 | [PUB] [PDF] [CODE] |
| Déni de service ou contrôle précis : vers des attaques d'empoisonnement de modèles flexibles sur l'apprentissage fédéré | Université de Xiangtan | IJCAI | 2023 | [PUB] [PDF] [CODE] |
| FedHGN : un cadre fédéré pour les réseaux de neurones à graphes hétérogènes | CUHK | IJCAI | 2023 | [PUB] [PDF] [CODE] |
| FedET : un cadre d'apprentissage incrémentiel de classe fédérée efficace en matière de communication basé sur un transformateur amélioré | Technologie Ping An ; JEU | IJCAI | 2023 | [PUB] [PDF] |
| Apprentissage fédéré rapide pour les prévisions météorologiques : vers des modèles de base sur les données météorologiques | UTS | IJCAI | 2023 | [PUB] [PDF] [CODE] |
| FedBFPT : un cadre d'apprentissage fédéré efficace pour la pré-formation continue de Bert | ZJU | IJCAI | 2023 | [PUB] [CODE] |
| Apprentissage fédéré bayésien : une enquête | Piste d'enquête IJCAI | 2023 | [PDF] | |
| Une enquête sur l'évaluation fédérée dans l'apprentissage fédéré | Université Macquarie | Piste d'enquête IJCAI | 2023 | [PUB] [PDF] |
| SAMBA : Un cadre générique pour les bandits multi-armés fédérés sécurisés (Extended Abstract) | INSA Centre Val de Loire | Piste du journal IJCAI | 2023 | [PUB] |
| Le coût des communications en termes de sécurité et de confidentialité dans l'estimation de fréquence fédérée | Stanford | AISTATS | 2023 | [PUB] [CODE] |
| Apprentissage fédéré efficace et léger via un abandon distribué asynchrone | Université du riz | AISTATS | 2023 | [PUB] [CODE] |
| Apprentissage fédéré sous dérive de concepts distribués | CMU | AISTATS | 2023 | [PUB] [CODE] |
| Caractériser les attaques d’évasion interne dans l’apprentissage fédéré | CMU | AISTATS | 2023 | [PUB] [CODE] |
| Federated Asymptotics : un modèle pour comparer les algorithmes d'apprentissage fédéré | Stanford | AISTATS | 2023 | [PUB] [CODE] |
| Apprentissage fédéré privé non convexe sans serveur de confiance | USC | AISTATS | 2023 | [PUB] [CODE] |
| Apprentissage fédéré pour les flux de données | Université ́ e Côte d'Azur | AISTATS | 2023 | [PUB] [CODE] |
| Rien que des regrets – Découverte causale fédérée préservant la confidentialité | Centre Helmholtz pour la sécurité de l'information | AISTATS | 2023 | [PUB] [CODE] |
| Attaque d'inférence d'adhésion active dans le cadre de la confidentialité différentielle locale dans l'apprentissage fédéré | UFL | AISTATS | 2023 | [PUB] [CODE] |
| Federated Averaging Langevin Dynamics : Vers une théorie unifiée et de nouveaux algorithmes | CMAP | AISTATS | 2023 | [PUB] |
| Apprentissage fédéré robuste et byzantin avec des taux statistiques optimaux | Université de Berkeley | AISTATS | 2023 | [PUB] [CODE] |
| Apprentissage fédéré sur des graphiques non-IID via le partage de connaissances structurelles | UTS | AAAI | 2023 | [PDF] [CODE] |
| FedGS : échantillonnage fédéré basé sur des graphiques avec disponibilité arbitraire des clients | XMU | AAAI | 2023 | [PDF] [CODE] |
| Crowdsourcing fédéré boosté par des incitations | SDU | AAAI | 2023 | [PDF] |
| Vers une compréhension de la sélection biaisée des clients dans l'apprentissage fédéré. | CMU | AISTATS | 2022 | [PUB] [CODE] |
| FLIX : une alternative simple et efficace en matière de communication aux méthodes locales d'apprentissage fédéré | KAUST | AISTATS | 2022 | [PUB] [PDF] [CODE] |
| Limites nettes pour la moyenne fédérée (SGD locale) et la perspective continue. | Stanford | AISTATS | 2022 | [PUB] [PDF] [CODE] |
| Apprentissage par renforcement fédéré avec hétérogénéité de l'environnement. | PCU | AISTATS | 2022 | [PUB] [PDF] [CODE] |
| Détection de communauté myope fédérée avec communication ponctuelle | Purdue | AISTATS | 2022 | [PUB] [PDF] |
| Algorithmes asynchrones liés à une confiance supérieure pour les bandits linéaires fédérés. | Université de Virginie | AISTATS | 2022 | [PUB] [PDF] [CODE] |
| Vers un apprentissage de la structure de réseau bayésien fédéré avec optimisation continue. | CMU | AISTATS | 2022 | [PUB] [PDF] [CODE] |
| Apprentissage fédéré avec agrégation asynchrone tamponnée | Méta-IA | AISTATS | 2022 | [PUB] [PDF] [VIDÉO] |
| Apprentissage fédéré différentiellement privé sur des données hétérogènes. | Stanford | AISTATS | 2022 | [PUB] [PDF] [CODE] |
| SparseFed : Atténuer les attaques d'empoisonnement des modèles dans l'apprentissage fédéré grâce à la sparsification | Princeton | AISTATS | 2022 | [PUB] [PDF] [CODE] [VIDÉO] |
| La base compte : de meilleures méthodes de second ordre efficaces en matière de communication pour l'apprentissage fédéré | KAUST | AISTATS | 2022 | [PUB] [PDF] |
| Augmentation du gradient fonctionnel fédéré. | Université de Pennsylvanie | AISTATS | 2022 | [PUB] [PDF] [CODE] |
| QLSD : Dynamique stochastique de Langevin quantifiée pour l'apprentissage fédéré bayésien. | Laboratoire d'IA Criteo | AISTATS | 2022 | [PUB] [PDF] [CODE] [VIDÉO] |
Extrapolation de connaissances basée sur le méta-apprentissage pour les graphiques de connaissances dans le cadre fédéré kg. | ZJU | IJCAI | 2022 | [PUB] [PDF] [CODE] |
| Apprentissage fédéré personnalisé avec un graphique | UTS | IJCAI | 2022 | [PUB] [PDF] [CODE] |
| Réseau de neurones graphiques fédérés verticalement pour la classification des nœuds préservant la confidentialité | Zju | Ijcai | 2022 | [Pub] [PDF] |
| S'adapter à l'adaptation: la personnalisation d'apprentissage pour l'apprentissage fédéré inter-silo | Ijcai | 2022 | [Pub] [PDF] [Code] | |
| Transfert de connaissances d'ensemble hétérogène pour la formation de grands modèles à l'apprentissage fédéré | Ijcai | 2022 | [Pub] [PDF] | |
| Apprentissage fédéré privé semi-supervisé. | Ijcai | 2022 | [PUB] | |
| Apprentissage fédéré continu basé sur la distillation des connaissances. | Ijcai | 2022 | [PUB] | |
| Apprentissage fédéré sur des données hétérogènes et à longue queue via le classificateur Retaining avec des fonctionnalités fédérées | Ijcai | 2022 | [Pub] [PDF] [Code] | |
| Attention à plusieurs tâches fédérées pour la reconnaissance de l'activité humaine inter-individuelle | Ijcai | 2022 | [PUB] | |
| Apprentissage fédéré personnalisé avec généralisation contextualisée. | Ijcai | 2022 | [Pub] [PDF] | |
| Sticien d'apprentissage fédéré: agrégation robuste avec sélection adaptative du client. | Ijcai | 2022 | [Pub] [PDF] | |
| FEDCG: Tirez parti du GAn conditionnel pour protéger la vie privée et maintenir des performances concurrentielles dans l'apprentissage fédéré | Ijcai | 2022 | [Pub] [PDF] [Code] | |
| FedDuap: apprentissage fédéré avec mise à jour dynamique et élagage adaptatif à l'aide de données partagées sur le serveur. | Ijcai | 2022 | [Pub] [PDF] | |
Vers l'apprentissage fédéré vérifiable surv. | Ijcai | 2022 | [Pub] [PDF] | |
| Harmofl: harmoniser les dérives locales et mondiales dans l'apprentissage fédéré sur les images médicales hétérogènes | Cuhk; Buaa | AAAI | 2022 | [Pub] [PDF] [Code] [解读] |
| Apprentissage fédéré pour la reconnaissance faciale avec correction du gradient | Se dérober | AAAI | 2022 | [Pub] [PDF] |
| ScreadGnn: apprentissage fédéré à plusieurs tâches décentralisé pour les réseaux de neurones graphiques sur les données moléculaires | USC | AAAI | 2022 | [Pub] [PDF] [Code] [解读] |
| SmartIDX: Réduire le coût de la communication dans l'apprentissage fédéré en exploitant les structures CNNS | FRAPPER; PCL | AAAI | 2022 | [Pub] [Code] |
| Bridging entre les signaux de traitement cognitif et les fonctionnalités linguistiques via un réseau attentionnel unifié | Tju | AAAI | 2022 | [Pub] [PDF] |
| Saisir des périodes d'apprentissage critiques dans l'apprentissage fédéré | Université SUNY-Binghamton | AAAI | 2022 | [Pub] [PDF] |
| Coordination des moments pour l'apprentissage fédéré inter-silo | Université de Pittsburgh | AAAI | 2022 | [Pub] [PDF] |
| Fedproto: apprentissage du prototype fédéré sur des dispositifs hétérogènes | Uts | AAAI | 2022 | [Pub] [PDF] [Code] |
| FedSoft: apprentissage fédéré en grappes avec mise à jour locale proximale | CMU | AAAI | 2022 | [Pub] [PDF] [Code] |
| Formation clairsemée dynamique fédérée: calculer moins, communiquer moins, mais apprendre mieux | Université du Texas à Austin | AAAI | 2022 | [Pub] [PDF] [Code] |
| Fedfr: Optimisation des articles Federated Framework pour la reconnaissance faciale générique et personnalisée | Université nationale de Taïwan | AAAI | 2022 | [Pub] [PDF] [Code] |
| Splitfed: lorsque l'apprentissage fédéré rencontre l'apprentissage divisé | Csiro | AAAI | 2022 | [Pub] [PDF] [Code] |
| Planification efficace des appareils avec apprentissage fédéré multi-emplois | Université de Soochow | AAAI | 2022 | [Pub] [PDF] |
| Alignement de gradient implicite dans l'apprentissage distribué et fédéré | Iit kanpur | AAAI | 2022 | [Pub] [PDF] |
| Classification du voisin le plus proche fédéré avec une colonie de fruits | Recherche IBM | AAAI | 2022 | [Pub] [PDF] [Code] |
| Itérated Vector Fields and Conservatisme, avec des applications à l'apprentissage fédéré. | Alt | 2022 | [Pub] [PDF] | |
| Apprentissage fédéré avec confidentialité et optimisation adaptative amplifiées par la sparsification | Ijcai | 2021 | [Pub] [PDF] [Vidéo] | |
| Le comportement imite la distribution: combiner les comportements individuels et de groupe pour l'apprentissage fédéré | Ijcai | 2021 | [Pub] [PDF] | |
| FedSpeech: Texte à dispection fédéré avec apprentissage continu | Ijcai | 2021 | [Pub] [PDF] | |
| Apprentissage fédéré à un seul coup pour le cadre inter-silo | Ijcai | 2021 | [Pub] [PDF] [Code] | |
| Distillation du modèle fédéré avec une confidentialité différentielle sans bruit | Ijcai | 2021 | [Pub] [PDF] [Vidéo] | |
| LDP-FL: Aggrégation privée pratique dans l'apprentissage fédéré avec une confidentialité différentielle locale | Ijcai | 2021 | [Pub] [PDF] | |
| Apprentissage fédéré avec une moyenne équitable. | Ijcai | 2021 | [Pub] [PDF] [Code] | |
| H-FL: Une architecture hiérarchique économe en communication et protégée par la confidentialité pour l'apprentissage fédéré. | Ijcai | 2021 | [Pub] [PDF] | |
| Apprentissage de bord fédéré économe en communication et évolutif. | Ijcai | 2021 | [PUB] | |
| Sécuriser l'apprentissage fédéré vertical asynchrone avec une mise à jour arrière | Université Xidian; Tech JD | AAAI | 2021 | [Pub] [PDF] [Vidéo] |
| FedRec ++: recommandation fédérée sans perte avec rétroaction explicite | Szu | AAAI | 2021 | [Pub] [vidéo] |
| Bandits multi-armés fédérés | Université de Virginie | AAAI | 2021 | [Pub] [PDF] [Code] [Video] |
| Sur la convergence du SGD local économe en communication pour l'apprentissage fédéré | Université du temple; Université de Pittsburgh | AAAI | 2021 | [Pub] [vidéo] |
| Flame: apprentissage fédéré différentiellement privé dans le modèle de shuffle | Université Renmin de Chine; Université de Kyoto | AAAI | 2021 | [Pub] [PDF] [Video] [Code] |
| Vers la compréhension de l'influence des clients individuels dans l'apprentissage fédéré | Sjtu; Université du Texas à Dallas | AAAI | 2021 | [Pub] [PDF] [Vidéo] |
| Apprentissage fédéré de manière indispensable contre les clients malveillants | Université Duke | AAAI | 2021 | [Pub] [PDF] [VIDEO] [Slide] |
| Apprentissage fédéré par Silo personnalisé sur des données non IID | Université Simon Fraser; Université McMaster | AAAI | 2021 | [Pub] [PDF] [VIDEO] [UC.] |
| Jeux de partage du modèle: analyser l'apprentissage fédéré sous participation volontaire | Université Cornell | AAAI | 2021 | [Pub] [PDF] [Code] [Video] |
| Malédiction ou rachat? Comment l'hétérogénéité des données affecte la robustesse de l'apprentissage fédéré | Université du Nevada; Recherche IBM | Aaai | 2021 | [Pub] [PDF] [Vidéo] |
| Jeu de gradients: atténuer les clients non pertinents dans l'apprentissage fédéré | Iit bombay; Recherche IBM | Aaai | 2021 | [Pub] [PDF] [Code] [Video] [Supp] |
| Federated Block Coordonnées Schéma de descente pour l'apprentissage des modèles globaux et personnalisés | Cuhk; Université d'État de l'Arizona | Aaai | 2021 | [Pub] [PDF] [Video] [Code] |
| Aborder le déséquilibre des cours dans l'apprentissage fédéré | Université du Nord-Ouest | Aaai | 2021 | [Pub] [PDF] [VIDEO] [CODE] [解读] |
| Défendre contre les raies dans l'apprentissage fédéré avec un taux d'apprentissage robuste | Université du Texas à Dallas | Aaai | 2021 | [Pub] [PDF] [Video] [Code] |
| Attaques en libre-conducteur contre l'agrégation de modèles dans l'apprentissage fédéré | Laboratoires d'accenture | Aistates | 2021 | [Pub] [PDF] [Code] [Video] [Supp] |
| Fédération F-Diffférentielle intimité | Université de Pennsylvanie | Aistates | 2021 | [Pub] [Code] [Video] [Supp] |
| Apprentissage fédéré avec compression: analyse unifiée et garanties pointues | Université d'État de Pennsylvanie; Université du Texas à Austin | Aistates | 2021 | [Pub] [PDF] [Code] [Video] [Supp] |
| Modèle mélangé de confidentialité différentielle dans l'apprentissage fédéré | Ucla; Google | Aistates | 2021 | [Pub] [vidéo] [Supp] |
| Convergence et compromis de précision dans l'apprentissage fédéré et le méta-apprentissage | Aistates | 2021 | [Pub] [PDF] [VIDEO] [Supp] | |
| Bandits multi-armés fédérés avec personnalisation | Université de Virginie; Université d'État de Pennsylvanie | Aistates | 2021 | [Pub] [PDF] [Code] [Video] [Supp] |
| Vers la participation flexible des appareils à l'apprentissage fédéré | CMU; Sysu | Aistates | 2021 | [Pub] [PDF] [VIDEO] [Supp] |
| Méta-apprentissage fédéré pour la détection de carte de crédit frauduleuse | Ijcai | 2020 | [Pub] [vidéo] | |
| Un jeu multi-joueurs pour étudier les schémas d'incitation à l'apprentissage fédéré | Ijcai | 2020 | [Pub] [Code] [解读] | |
| Arbres de décision de stimulation de gradient fédéré pratique | Nus; Uwa | Aaai | 2020 | [Pub] [PDF] [Code] |
| Apprentissage fédéré pour les problèmes de mise à la terre de la vision et du langage | Pku; Tencent | Aaai | 2020 | [PUB] |
| Attribution fédérée de Dirichlet latente: un cadre local basé sur la confidentialité | Buaa | Aaai | 2020 | [PUB] |
| Hachage des patients fédérés | Université Cornell | Aaai | 2020 | [PUB] |
| Apprentissage fédéré robuste via l'enseignement de la machine collaborative | Symantec Research Labs; Kauste | Aaai | 2020 | [Pub] [PDF] |
| FedVision: une plate-forme de détection d'objets visuels en ligne propulsée par l'apprentissage fédéré | Webank | Aaai | 2020 | [Pub] [PDF] [Code] |
| FedPaq: une méthode d'apprentissage fédérée économe en communication avec une moyenne et une quantification périodiques | UC Santa Barbara; UT Austin | Aistates | 2020 | [Pub] [PDF] [VIDEO] [Supp] |
| Comment faire un apprentissage fédéré par porte dérobée | Cornell Tech | Aistates | 2020 | [Pub] [PDF] [Video] [Code] [Supp] |
| Découverte des frappeurs lourds fédérés avec une confidentialité différentielle | RPI; Google | Aistates | 2020 | [Pub] [PDF] [VIDEO] [Supp] |
| Visualisation multi-agents pour expliquer l'apprentissage fédéré | Webank | Ijcai | 2019 | [Pub] [vidéo] |
Federated Learning Papers acceptés par la conférence et le Journal de la Top ML (Machine Learning), y compris les Neirips (Conférence annuelle sur les systèmes de traitement de l'information neuronale), ICML (Conférence internationale sur l'apprentissage automatique), ICLR (Conférence internationale sur les représentations de l'apprentissage), COLT (Conférence annuelle Computationnelle Théorie de l'apprentissage), UAI (Conférence sur l'incertitude en intelligence artificielle), Machine Learning, JMLR (Journal of Machine Learning Research), TPAMI (transactions IEEE sur l'analyse des modèles et l'intelligence machine).
| Titre | Affiliation | Lieu | Année | Matériels |
|---|---|---|---|---|
| Stabiliser et accélérer l'apprentissage fédéré sur les données hétérogènes avec participation partielle au client | Tpami | 2025 | [PUB] | |
| Modèle fédéré médical avec mélange de composants personnalisés et partagés | Tpami | 2025 | [PUB] | |
| Apprentissage fédéré à un seul | Nezier | 2024 | [PUB] | |
| Apprentissage fédéré non convexe sur des sous-manifolds lisses compacts avec des données hétérogènes | Nezier | 2024 | [PUB] | |
| FEDGMKD: Un cadre d'apprentissage fédéré efficace efficace à travers la distillation des connaissances et l'agrégation consciente de l'écart | Nezier | 2024 | [PUB] | |
| Amélioration de la généralisation de l'apprentissage fédéré avec une régularisation des informations mutuelles de données modèles: une approche d'inférence postérieure | Nezier | 2024 | [PUB] | |
| Modèle fédéré Hétérogène Matryoshka Représentation Apprentissage | Nezier | 2024 | [PUB] | |
| Apprentissage du graphique fédéré pour recommandation de domaine croisé | Nezier | 2024 | [PUB] | |
| Fedgmark: Affaire à la filigrane certifiée pour l'apprentissage du graphique fédéré | Nezier | 2024 | [PUB] | |
| Adaptateur à double personne pour les modèles de fondation fédérés | Nezier | 2024 | [PUB] | |
| Federated Natural Policy Gradient et acteur Critic Methods for Multi-tâle Renfort Learning | Nezier | 2024 | [PUB] | |
| Apprivoiser la longue queue dans la prédiction de la mobilité humaine | Nezier | 2024 | [PUB] | |
| Double défense: améliorer la vie privée et atténuer les attaques d'empoisonnement dans l'apprentissage fédéré | Nezier | 2024 | [PUB] | |
| Optimisateurs améliorés pour les graphiques pour la recommandation consciente de la structure Evolution | Nezier | 2024 | [PUB] | |
| DOFIT: réglage des instructions fédérées au domaine avec l'oubli catastrophique atténué | Nezier | 2024 | [PUB] | |
| Apprentissage fédéré efficace contre l'indisponibilité du client hétérogène et non stationnaire | Nezier | 2024 | [PUB] | |
| Transformateur fédéré: apprentissage fédéré vertical multipartite sur des données pratiques liées floues | Nezier | 2024 | [PUB] | |
| Fiarse: Apprentissage fédéré hétérogène modèle via l'extraction du sous-modèle d'importance | Nezier | 2024 | [PUB] | |
| Fédément fédéré probabiliste avec des données non IID et déséquilibrées | Nezier | 2024 | [PUB] | |
| Flora: modèles fédérés de grande langue à réglage fin avec des adaptations de bas rang hétérogène | Nezier | 2024 | [PUB] | |
| Taming Cross-Domain Représentation Variance de l'apprentissage du prototype fédéré avec des domaines de données hétérogènes | Nezier | 2024 | [PUB] | |
| PFEDCLUB: Aggrégation de modèle hétérogène contrôlable pour l'apprentissage fédéré personnalisé | Nezier | 2024 | [PUB] | |
| Pourquoi aller plein? Élever l'apprentissage fédéré à travers des mises à jour de réseau partiel | Nezier | 2024 | [PUB] | |
| FUSELL: apprentissage fédéré à un coup à travers la lentille de la causalité avec une fusion de modèle progressive | Nezier | 2024 | [PUB] | |
| FEDSSP: Apprentissage du graphique fédéré avec connaissance spectrale et préférence personnalisée | Nezier | 2024 | [PUB] | |
| Gestion des apprentissage des espaces de caractéristiques hétérogènes avec une exploitation explicite d'étiquette | Nezier | 2024 | [PUB] | |
| A-FEDPD: L'alignement à double dérive est tous les besoins d'apprentissage du dual primal fédéré | Nezier | 2024 | [PUB] | |
| Estimation de fréquence privée et personnalisée dans un cadre fédéré | Nezier | 2024 | [PUB] | |
| Le complexe de complexité de la communication de l'échantillon dans le Q-Lederated Q-Learning | Nezier | 2024 | [PUB] | |
| Apprentissage de renforcement hors ligne de l'ensemble fédéré | Nezier | 2024 | [PUB] | |
| Adaptation fédérée à la boîte noire pour la segmentation sémantique | Nezier | 2024 | [PUB] | |
| Penser à l'avant: Finetuning fédéré économe en mémoire des modèles de langue | Nezier | 2024 | [PUB] | |
| Apprentissage fédéré des modèles de fondation en langue visuelle: analyse théorique et méthode | Nezier | 2024 | [PUB] | |
| Conception optimale pour l'élicitation des préférences humaines | Nezier | 2024 | [PUB] | |
| Vers divers appareils apprentissage fédéré hétérogène via une tâche intégration des connaissances arithmétiques | Nezier | 2024 | [PUB] | |
| Apprentissage fédéré personnalisé via l'adaptation de la distribution des fonctionnalités | Nezier | 2024 | [PUB] | |
| SCAFFLSA: Hétérogénéité de l'approvisionnement en approximation stochastique linéaire fédérée et apprentissage TD | Nezier | 2024 | [PUB] | |
| Une approche bayésienne pour l'apprentissage fédéré personnalisé dans des contextes hétérogènes | Nezier | 2024 | [PUB] | |
| RFLPA: un cadre d'apprentissage fédéré robuste contre les attaques d'empoisonnement avec une agrégation sécurisée | Nezier | 2024 | [PUB] | |
| Fedgtst: stimuler la transférabilité globale des modèles fédérés via le réglage des statistiques | Nezier | 2024 | [PUB] | |
| Clustering d'apprentissage de bout en bout pour l'apprentissage de l'intention dans la recommandation | Nezier | 2024 | [PUB] | |
| FedLPA: apprentissage fédéré à un coup avec agrégation postérieure par couche | Nezier | 2024 | [PUB] | |
| Time-FFM: Vers le modèle de fondation fédéré de LM à LM pour les prévisions de séries chronologiques | Nezier | 2024 | [PUB] | |
| FOOGD: Collaboration fédérée pour la généralisation et la détection hors distribution | Nezier | 2024 | [PUB] | |
| Un couteau à armée suisse pour l'apprentissage fédéré hétérogène: couplage flexible via la norme trace | Nezier | 2024 | [PUB] | |
| Fedne: voisin fédéré assisté de substitution incorporer la réduction de la dimensionnalité | Nezier | 2024 | [PUB] | |
| La formation locale à faible précision est suffisante pour l'apprentissage fédéré | Nezier | 2024 | [PUB] | |
| Apprentissage auto-supervisé fédéré par les ressources avec des représentations de classe mondiales | Nezier | 2024 | [PUB] | |
| Sur la nécessité de la collaboration pour la sélection des modèles en ligne avec des données décentralisées | Nezier | 2024 | [PUB] | |
| Le pouvoir de l'extrapolation dans l'apprentissage fédéré | Nezier | 2024 | [PUB] | |
| (Fl) $ ^ 2 $: surmonter quelques étiquettes dans l'apprentissage semi-supervisé fédéré | Nezier | 2024 | [PUB] | |
| Sur les stratégies d'échantillonnage pour le rétrécissement du modèle spectral | Nezier | 2024 | [PUB] | |
| Personnalisation des modèles de langue avec LORA par instance pour une recommandation séquentielle | Nezier | 2024 | [PUB] | |
| SPAFL: apprentissage fédéré économe en communication avec des modèles clairsemés et faibles frais généraux de calcul | Nezier | 2024 | [PUB] | |
| Hydra-FL: Distillation de connaissances hybrides pour un apprentissage fédéré robuste et précis | Nezier | 2024 | [PUB] | |
| Méthodes de point proximal stabilisé pour l'optimisation fédérée | Nezier | 2024 | [PUB] | |
| Dapperfl: Domain Adaptive Federated Lederad with Model Fusion Troping for Edge Devices | Nezier | 2024 | [PUB] | |
| Dissection des disparités de paramètres pour la défense de la porte dérobée dans l'apprentissage fédéré hétérogène | Nezier | 2024 | [PUB] | |
| L'agent le plus performant mène-t-il le pack? Analyser la dynamique des agents dans SGD distribué unifié | Nezier | 2024 | [PUB] | |
| FEDAVP: Augmentez les données locales via une politique partagée dans l'apprentissage fédéré | Nezier | 2024 | [PUB] | |
| Cobo: apprentissage collaboratif via l'optimisation du bilevelle | Nezier | 2024 | [PUB] | |
| Analyse de convergence de l'apprentissage fédéré divisé sur les données hétérogènes | Nezier | 2024 | [PUB] | |
| Optimisation robuste de groupe fédérée économe en communication | Nezier | 2024 | [PUB] | |
| Ferrari: fonctionnalité fédérée désapprentissage via l'optimisation de la sensibilité des fonctionnalités | Nezier | 2024 | [PUB] | |
| Apprentissage fédéré sur les modes connectés | Nezier | 2024 | [PUB] | |
| Apprentissage fédéré personnalisé avec mélange de modèles pour la prédiction adaptative et le réglage des modèles | Nezier | 2024 | [PUB] | |
| L'équité égalitaire mène-t-elle à l'instabilité? L'équité limite l'apprentissage fédéré stable sous des comportements altruistes | Nezier | 2024 | [PUB] | |
| Prédiction en ligne fédérée d'experts ayant une confidentialité différentielle: séparations et regrets accélérants | Nezier | 2024 | [PUB] | |
| DataStaging: Volez les données des modèles de diffusion dans l'apprentissage fédéré avec plusieurs chevaux de Troie | Nezier | 2024 | [PUB] | |
| Plan comportementaux fédérés: expliquer l'évolution du comportement du client dans l'apprentissage fédéré | Nezier | 2024 | [PUB] | |
| Apprentissage fédéré hiérarchique avec correction de gradient à plusieurs reprises | Nezier | 2024 | [PUB] | |
| Hyperprim: un cadre d'agrégation non linéaire adaptatif pour l'apprentissage automatique distribué sur des données non IID et des liens de communication variant dans le temps | Nezier | 2024 | [PUB] | |
| Spear: inversion du gradient exact des lots dans l'apprentissage fédéré | Nezier | 2024 | [PUB] | |
| Apprentissage fédéré sous Participation périodique du client et données hétérogènes: un nouvel algorithme et analyse économes en communication | Nezier | 2024 | [PUB] | |
| ECSATIONS PARRIGHTS: regroupement multi-visualités fédéré dans des vues hybrides hétérogènes | Nezier | 2024 | [PUB] | |
| Apprentissage fédéré résistant à la confusion via l'harmonisation des données basée sur la diffusion sur les données non IID | Nezier | 2024 | [PUB] | |
| Soupes supérieures locales: un catalyseur de fusion de modèles dans l'apprentissage fédéré croisé | Nezier | 2024 | [PUB] | |
| Formation de collaboration en libre-vider et en conflit pour l'apprentissage fédéré inter-silo | Nezier | 2024 | [PUB] | |
| Clustering du classificateur et alignement des fonctionnalités pour l'apprentissage fédéré sous la dérive de concept distribué | Nezier | 2024 | [PUB] | |
| Échantillonnage du client guidé par l'hétérogénéité: vers un apprentissage fédéré rapide et efficace | Nezier | 2024 | [PUB] | |
| Fait ou fiction: les mécanismes véridiques peuvent-ils éliminer la conduite libre fédérée? | Nezier | 2024 | [PUB] | |
| Apprentissage de préférence active pour commander des articles dans et hors échantillon | Nezier | 2024 | [PUB] | |
| Fination Fenderated Fining of Grand Language Models sous des tâches hétérogènes et des ressources du client | Nezier | 2024 | [PUB] | |
| Personnalisation finale dans l'apprentissage fédéré pour atténuer les clients adversaires | Nezier | 2024 | [PUB] | |
| Revisiter l'ensemble dans un apprentissage fédéré à un seul coup | Nezier | 2024 | [PUB] | |
| Fedllm-Bench: repères réalistes pour l'apprentissage fédéré de modèles de langue importants | Nezier | 2024 | [PUB] | |
| $ exttt {pfl-research} $: framework de simulation pour accélérer la recherche en apprentissage fédéré privé | Nezier | 2024 | [PUB] | |
| Fedmeki: une référence pour la mise à l'échelle des modèles de fondations médicales via l'injection de connaissances fédérée | Nezier | 2024 | [PUB] | |
| Approximation Momentum dans l'apprentissage fédéré privé asynchrone | Atelier de Neirips | 2024 | [PUB] | |
| Squeeze de la cohorte: au-delà d'un seul tour de communication par cohorte dans l'apprentissage fédéré croisé | Atelier de Neirips | 2024 | [PUB] | |
| Apprentissage fédéré avec contenu génératif | Atelier de Neirips | 2024 | [PUB] | |
| Tirer parti des données de texte non structurées pour le réglage de l'instruction fédérée des modèles de grande langue | Atelier de Neirips | 2024 | [PUB] | |
| Attaque et défense de sécurité émergentes dans le réglage des instructions fédérées des modèles de gros langues | Atelier de Neirips | 2024 | [PUB] | |
| Collaboration sans défection entre les concurrents dans un système d'apprentissage | Atelier de Neirips | 2024 | [PUB] | |
| Sur les taux de convergence de l'apprentissage Q fédéré dans des environnements hétérogènes | Atelier de Neirips | 2024 | [PUB] | |
| ENCCLUSTER: Apporter un chiffrement fonctionnel dans les modèles fondamentaux fédérés | Atelier de Neirips | 2024 | [PUB] | |
| Ferret: réglage fédéré par-paramètre à grande échelle pour les modèles de grande langue | Atelier de Neirips | 2024 | [PUB] | |
| Apprentissage fédéré chaud et enfichable | Atelier de Neirips | 2024 | [PUB] | |
| Formation dynamique de faible rang fédérée avec les garanties de convergence de perte mondiale | Atelier de Neirips | 2024 | [PUB] | |
| L'avenir du modèle de grande langue pré-formation est fédéré | Atelier de Neirips | 2024 | [PUB] | |
| Apprentissage collaboratif avec des représentations linéaires partagées: taux statistiques et algorithmes optimaux | Atelier de Neirips | 2024 | [PUB] | |
| Le phénomène SynapticCity: lorsque tous les modèles de fondation épousent l'apprentissage fédéré et la blockchain | Atelier de Neirips | 2024 | [PUB] | |
| Zoopfl: Exploration des modèles de fondation noire pour l'apprentissage fédéré personnalisé | Atelier de Neirips | 2024 | [PUB] | |
| Decomfl: apprentissage fédéré avec communication sans dimension | Atelier de Neirips | 2024 | [PUB] | |
| Améliorer la connectivité du groupe pour la généralisation de l'apprentissage en profondeur fédéré | Atelier de Neirips | 2024 | [PUB] | |
| Carte: Modèle de fusion avec le front de Pareto amorti en utilisant un calcul limité | Atelier de Neirips | 2024 | [PUB] | |
| OPA: agrégation privée à un coup avec interaction unique du client et ses applications à l'apprentissage fédéré | Atelier de Neirips | 2024 | [PUB] | |
| Élagage du modèle hybride adaptatif dans l'apprentissage fédéré par l'exploration des pertes | Atelier de Neirips | 2024 | [PUB] | |
| Formation fédérée mondiale des modèles de langue | Atelier de Neirips | 2024 | [PUB] | |
| FedStein: Amélioration de l'apprentissage fédéré multi-domaines via James-Stein Estimator | Atelier de Neirips | 2024 | [PUB] | |
| Amélioration de la découverte causale dans les paramètres fédérés avec des échantillons locaux limités | Atelier de Neirips | 2024 | [PUB] | |
| $ exttt {pfl-research} $: framework de simulation pour accélérer la recherche en apprentissage fédéré privé | Atelier de Neirips | 2024 | [PUB] | |
| DMM: Mécanisme matriciel distribué pour l'apprentissage fédéré différentiellement privé à l'aide de partage secret emballé | Atelier de Neirips | 2024 | [PUB] | |
| FEDCBO: Atteindre le consensus de groupe dans l'apprentissage fédéré en grappe par optimisation basée sur le consensus | Jmlr | 2024 | [PUB] | |
| Correspondance de graphes fédérés efficaces | ICML | 2024 | [PUB] | |
| Comprendre l'apprentissage fédéré assisté par le serveur en présence d'une participation incomplète du client | ICML | 2024 | [PUB] | |
| Au-delà de la fédération: apprentissage fédéré conscient de la topologie pour la généralisation aux clients invisibles | ICML | 2024 | [PUB] | |
| FedBPT: réglage rapide de la boîte noire efficace pour les modèles de gros langues | ICML | 2024 | [PUB] | |
| L'hétérogénéité du modèle de pontage dans l'apprentissage fédéré via l'apprentissage de réciprocité asymétrique basée sur l'incertitude | ICML | 2024 | [PUB] | |
| Une nouvelle perspective théorique sur l'hétérogénéité des données dans l'optimisation fédérée | ICML | 2024 | [PUB] | |
| Amélioration de l'efficacité de stockage et de calcul dans l'apprentissage multimodal fédéré pour les modèles à grande échelle | ICML | 2024 | [] | |
| Momentum pour la victoire: apprentissage en renforcement fédéré collaboratif dans des environnements hétérogènes | ICML | 2024 | [PUB] | |
| Byzantine-Robust Federated Lederad: Impact du sous-échantillonnage des clients et des mises à jour locales | ICML | 2024 | [PUB] | |
| Avantages prouvables des étapes locales dans l'apprentissage fédéré hétérogène pour les réseaux de neurones: une perspective d'apprentissage des fonctionnalités | ICML | 2024 | [PUB] | |
| Accélérer l'apprentissage fédéré avec une estimation moyenne distribuée rapide | ICML | 2024 | [PUB] | |
| Apprentissage fédéré équitable via le noyau de veto proportionnel | ICML | 2024 | [PUB] | |
| Aegisfl: efficace et flexible préservant l'apprentissage fédéré byzantin-robuste | ICML | 2024 | [PUB] | |
| Récupération des étiquettes des mises à jour locales dans l'apprentissage fédéré | ICML | 2024 | [PUB] | |
| FedMbridge: apprentissage fédéré multimodal brilable | ICML | 2024 | [PUB] | |
| Harmoniser la généralisation et la personnalisation dans l'apprentissage rapide fédéré | ICML | 2024 | [PUB] | |
| Les perturbations mondiales estimées localement sont meilleures que les perturbations locales pour la minimisation de la netteté fédérée | ICML | 2024 | [PUB] | |
| Accélérer l'apprentissage fédéré hétérogène avec des classificateurs de forme fermée | ICML | 2024 | [PUB] | |
| Bandits multi-agents combinatoires fédérés | ICML | 2024 | [PUB] | |
| Une méthode de descente de gradient de composition stochastique doublement récursive pour l'optimisation de composition à plusieurs niveaux fédérée | ICML | 2024 | [PUB] | |
| Apprentissage fédéré hétérogène privé sans serveur de confiance revisité: algorithmes d'erreur optimaux et économes en communication pour les pertes convexes | ICML | 2024 | [PUB] | |
| FEDRC: aborder un défi de distribution diversifiés dans l'apprentissage fédéré par un regroupement robuste | ICML | 2024 | [PUB] | |
| Poursuivre le bien-être global dans l'apprentissage fédéré par la prise de décision séquentielle | ICML | 2024 | [PUB] | |
| Pré-texte: Modèles de langue de formation sur les données fédérées privées à l'ère des LLM | ICML | 2024 | [PUB] | |
| Aggrégation d'entropie autonome pour l'apprentissage fédéré hétérogène byzantin | ICML | 2024 | [PUB] | |
| Surmonter les données et les hétérogénéités du modèle dans l'apprentissage fédéré décentralisé via des ancres synthétiques | ICML | 2024 | [PUB] | |
| Optimisation fédérée avec correction de dérive doublement régularisée | ICML | 2024 | [PUB] | |
| FEDSC: Apprentissage auto-supervisé fédéré prouvable avec un objectif contrastif spectral sur les données non IID | ICML | 2024 | [PUB] | |
| Prédiction conforme fédérée parzantine-robuste byzantine | ICML | 2024 | [PUB] | |
| Atteindre la sparsification du gradient sans perte via la cartographie à l'espace alternatif dans l'apprentissage fédéré | ICML | 2024 | [PUB] | |
| Apprentissage fédéré en cluster via le partitionnement basé sur le gradient | ICML | 2024 | [PUB] | |
| Sorties précoces récurrentes pour l'apprentissage fédéré avec des clients hétérogènes | ICML | 2024 | [PUB] | |
| Repenser la recherche de minima à plat dans l'apprentissage fédéré | ICML | 2024 | [PUB] | |
| Fedbat: apprentissage fédéré économe en communication via une binarisation apprenable | ICML | 2024 | [PUB] | |
| Représentation fédérée Apprentissage dans le régime sous-paramétré | ICML | 2024 | [PUB] | |
| FedLMT: Aborder l'hétérogénéité du système de l'apprentissage fédéré via une formation de modèle de faible rang avec des garanties théoriques | ICML | 2024 | [PUB] | |
| Algorithme de bruit pour un apprentissage fédéré différentiellement privé différentiellement | ICML | 2024 | [PUB] | |
| Argent: réduction et application de variance en boucle unique à l'apprentissage fédéré | ICML | 2024 | [PUB] | |
| SIGNSGD avec défense fédérée: exploiter les attaques contradictoires par le décodage des panneaux de gradient | ICML | 2024 | [PUB] | |
| FedCal: Atteindre l'étalonnage local et global dans l'apprentissage fédéré via un échelle paramétrée agrégée | ICML | 2024 | [PUB] | |
| Apprentissage continu fédéré via un transfert de doubles connaissances basé sur une base | ICML | 2024 | [PUB] | |
| Réglage fédéré par-paramètre des modèles de langue de taille d'un milliard avec un coût de communication inférieur à 18 kilobytes | ICML | 2024 | [PUB] | |
| Maximisation sous-modulaire décomposable dans un cadre fédéré | ICML | 2024 | [PUB] | |
| Optimisation convexe stochastique privée et fédérée: stratégies efficaces pour les systèmes centralisés | ICML | 2024 | [PUB] | |
| Amélioration de la modélisation des ensembles de données fédérés à l'aide de mélanges-de-dirichlet-multinomiaux | ICML | 2024 | [PUB] | |
| Les leçons de l'analyse des erreurs de généralisation de l'apprentissage fédéré: vous pouvez communiquer moins souvent! | ICML | 2024 | [PUB] | |
| Resilient byzantin et à quelques coups de fiche rapide | ICML | 2024 | [PUB] | |
| Apprentissage invariant fédéré personnalisé à motivation causale avec une régularisation théorique des informations et des raccourcis | ICML | 2024 | [PUB] | |
| Sélection d'imitation des clients basée sur le classement pour un apprentissage fédéré efficace | ICML | 2024 | [PUB] | |
| Vers la théorie de l'apprentissage fédéré non supervisé: analyse non asymptotique des algorithmes EM fédérés | ICML | 2024 | [PUB] | |
| FADAS: Vers une optimisation asynchrone adaptative fédérée | ICML | 2024 | [PUB] | |
| Apprentissage de renforcement hors ligne fédéré: une couverture collaborative à une seule politique suffit | ICML | 2024 | [PUB] | |
| FedRedefense: défendre contre les attaques d'empoisonnement du modèle pour l'apprentissage fédéré à l'aide d'une erreur de reconstruction de mise à jour du modèle | ICML | 2024 | [PUB] | |
| MH-PFLID: modèle d'apprentissage fédéré personnalisé hétérogène par injection et distillation pour l'analyse des données médicales | ICML | 2024 | [PUB] | |
| Apprentissage neuro-symbolique fédéré | ICML | 2024 | [PUB] | |
| Personnalisation du groupe adaptatif pour l'apprentissage du transfert mutuel fédéré | ICML | 2024 | [PUB] | |
| Équilibrer la similitude et la complémentarité pour l'apprentissage fédéré | ICML | 2024 | [PUB] | |
| GNNS fédérés auto-exploitants avec des augmentations anti-shortcut | ICML | 2024 | [PUB] | |
| Un algorithme de composition à plusieurs niveaux stochastique fédéré pour la maximisation de l'ASUC profonde | ICML | 2024 | [PUB] | |
| Coala: une plate-forme d'apprentissage fédérée pratique et axée sur la vision | ICML | 2024 | [PUB] | |
| Apprentissage fédéré vertical asynchrone sécurisé et rapide via l'optimisation hybride en cascade | Mach Learn | 2024 | [PUB] | |
| Apprentissage fédéré en cluster économe en communication via la distance du modèle | Ustc; State Key Laboratory of Cognitive Intelligence | Mach Learn | 2024 | [PUB] |
| Apprentissage fédéré avec agrégation superquantile pour les données hétérogènes. | Recherche Google | Mach Learn | 2024 | [Pub] [PDF] [Code] |
| Alignement des sorties du modèle pour l'apprentissage fédéré non IID déséquilibré | Nju | Mach Learn | 2024 | [PUB] |
| Apprentissage fédéré des réseaux causaux linéaires généralisés | Tpami | 2024 | [PUB] | |
| Reconnaissance de l'activité humaine fédérée transversale | Tpami | 2024 | [PUB] | |
| Processus gaussien fédéré: convergence, personnalisation automatique et modélisation multi-fidélité | Université du Nord-Est ; Uom | Tpami | 2024 | [Pub] [PDF] [Code] |
| L'impact des attaques contradictoires contre l'apprentissage fédéré: une enquête | Iit | Tpami | 2024 | [PUB] |
| Comprendre et atténuer l'effondrement dimensionnel dans l'apprentissage fédéré | Nus | Tpami | 2024 | [Pub] [PDF] [Code] |
| Personne n'est laissé pour compte: apprentissage incité à la classe fédérée du monde réel | CAS; UCAS | Tpami | 2024 | [Pub] [PDF] [Code] |
| Généralitaire hétérogène fédéré hétérogène et apprentissage de similitude d'instance | Whu | Tpami | 2024 | [Pub] [PDF] [Code] |
| Apprentissage fédéré asynchrone en plusieurs étapes avec une confidentialité différentielle adaptative | HPU; Xjtu | Tpami | 2024 | [Pub] [PDF] [Code] |
| Un cadre d'apprentissage fédéré bayésien avec une approximation de Laplace en ligne | SUTISCH | Tpami | 2024 | [Pub] [PDF] [Code] |
| Amélioration de l'apprentissage fédéré à un seul coup à travers les données et la co-opinion d'ensemble | Ustc; Hkbu | ICLR | 2024 | [PUB] |
| Estimation de la confidentialité empirique unique pour l'apprentissage fédéré | ICLR | 2024 | [Pub] [PDF] | |
| Compétenance à un apprentissage fédéré avec compression de communication stochastique avec une compression fédérée avec compression de communication | LinkedIn; Upenn | ICLR | 2024 | [Pub] [PDF] |
| Une méthode légère pour lutter contre les statistiques de participation inconnues à la moyenne fédérée | IBM | ICLR | 2024 | [Pub] [PDF] [Code] |
| Une perspective d'information mutuelle sur l'apprentissage contrastif fédéré | Qualcomm | ICLR | 2024 | [PUB] |
| Algorithmes d'analyse comparative pour la généralisation du domaine fédéré | Université Purdue | ICLR | 2024 | [Pub] [PDF] [Code] |
| Apprentissage des arbres fédérés efficaces et efficaces sur les données hybrides | UC Berkeley | ICLR | 2024 | [Pub] [PDF] |
| Recommandation fédérée avec une personnalisation additive | Uts | ICLR | 2024 | [Pub] [PDF] [Code] |
| Aborder l'hétérogénéité des données dans l'apprentissage fédéré asynchrone avec l'étalonnage de mise à jour mis en cache | Bloc d'alimentation | ICLR | 2024 | [Pub] [Supp] |
| Formation orthogonale fédérée: atténuer l'oubli catastrophique mondial dans l'apprentissage fédéré continu | USC | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Oublier précis pour l'apprentissage continu fédéré hétérogène | JEU | ICLR | 2024 | [Pub] [Code] |
| Découverte causale fédérée à partir de données hétérogènes | Mbzuai | ICLR | 2024 | [Pub] [PDF] [Code] |
| Sur des bandits contextuels linéaires fédérés différentiellement privés | Université d'État de Wayne | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Communication véridique incitative pour les bandits fédérés | Université de Virginie | ICLR | 2024 | [Pub] [PDF] |
| Adaptation au domaine fédéré de principe: projection de gradient et poids automatique | Uiuc | ICLR | 2024 | [PUB] |
| Fedp3: élagage fédéré de réseau personnalisé et convivial pour la confidentialité sous l'hétérogénéité du modèle | Kauste | ICLR | 2024 | [PUB] |
| Génération rapide axée sur le texte pour les modèles de langue de vision dans l'apprentissage fédéré | Robert Bosch LLC | ICLR | 2024 | [Pub] [PDF] |
| Améliorer Lora dans l'apprentissage fédéré préservant la vie privée | Université du nord-est | ICLR | 2024 | [PUB] |
| Fedwon: apprentissage fédéré multi-domaines triomphant sans normalisation | Sony AI | ICLR | 2024 | [Pub] [PDF] |
| FedTrans: Estimation des services publics-transparent des clients pour un apprentissage fédéré robuste | Tu delft | ICLR | 2024 | [PUB] |
| FedCompass: apprentissage fédéré efficace sur le Silo sur des appareils clients hétérogènes à l'aide d'un planificateur de calcul de la puissance de l'informatique | Anl; Uiuc; NCSA | ICLR | 2024 | [Pub] [PDF] [Code] [Page] |
| Optimisation du corset bayésien pour l'apprentissage fédéré personnalisé | Iit bombay | ICLR | 2024 | [PUB] |
| Connectivité en mode linéaire par couche | Ruhr-Universtät Bochum | ICLR | 2024 | [Pub] [PDF] [Supp] |
| Faux It Till Make It: Federated Learning with Consensus Oriented Generation | Sjtu | ICLR | 2024 | [Pub] [PDF] |
| Se cacher à la vue: déguiser les données de vol d'attaques dans l'apprentissage fédéré | Insai | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Analyse à temps fini de l'apprentissage en renforcement fédéré hétérogène sur la politique | Université Columbia | ICLR | 2024 | [Pub] [PDF] |
| Apprentissage fédéré adaptatif avec des clients réglants automatiquement | Université de riz | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Apprentissage fédéré de porte dérobée en empoisonnant les couches critiques de la porte dérobée | ND | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Federateated Q-Learning: Linear Regret SpeedUp avec un faible coût de communication | PSU | ICLR | 2024 | [Pub] [Supp] [PDF] |
| FedImpro: mesurer et améliorer la mise à jour du client dans l'apprentissage fédéré | Hkbu | ICLR | 2024 | [Pub] [PDF] |
| Distance fédérée de Wasserstein | MIT | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Une analyse améliorée de l'écrasement par échantillon et par option dans l'apprentissage fédéré | DTU | ICLR | 2024 | [PUB] |
| FEDCDA: Apprentissage fédéré avec une agrégation de divergence croisée | NTU | ICLR | 2024 | [Pub] [Supp] |
| Gradients internes de couche transversale pour prolonger l'homogénéité à l'hétérogénéité dans l'apprentissage fédéré | HKU | ICLR | 2024 | [Pub] [PDF] |
| Momentum profite à l'apprentissage fédéré non IID simplement et prouvable | Pku; Upenn | ICLR | 2024 | [Pub] [PDF] |
| Optimisation du bandit non linéaire fédéré économe en communication | Université de Yale | ICLR | 2024 | [Pub] [PDF] |
| Évaluation des contributions équitables et efficaces pour l'apprentissage fédéré vertical | Huawei | ICLR | 2024 | [Pub] [Supp] [PDF] [Code] |
| Démystifier les compromis d'équité locale et mondiale dans l'apprentissage fédéré à l'aide de décomposition partielle des informations | Umcp | ICLR | 2024 | [Pub] [PDF] |
| Apprendre des représentations invariantes de causalité personnalisées pour les clients fédérés hétérogènes | Polyu | ICLR | 2024 | [PUB] |
| PEFLL: Apprentissage fédéré personnalisé en apprenant à apprendre | IST | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Méthodes d'accent sur le gradient économe en communication pour les inégalités variationnelles distribuées: analyse unifiée et mises à jour locales | Jhu | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Fedinverse: Évaluation des fuites de confidentialité dans l'apprentissage fédéré | USQ | ICLR | 2024 | [Pub] [Supp] |
| Fedda: méthodes de gradient adaptatif plus rapide pour l'optimisation contrainte fédérée | Umcp | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Formation robuste des modèles fédérés avec une carence extrêmement étiquetée | Hkbu | ICLR | 2024 | [Pub] [PDF] [Code] |
| Comprendre la convergence et la généralisation dans l'apprentissage fédéré par la théorie de l'apprentissage des fonctionnalités | Riken AIP | ICLR | 2024 | [PUB] |
| Enseigner les LLM à Phish: voler des informations privées à des modèles linguistiques | Université de Princeton | ICLR | 2024 | [PUB] |
| Comme l'huile et l'eau: les méthodes de robustesse du groupe et les défenses d'empoisonnement ne se mélangent pas | Umcp | ICLR | 2024 | [PUB] |
| Convergence accélérée de la méthode de balle lourde stochastique sous bruit de gradient anisotrope | Hkust | ICLR | 2024 | [Pub] [PDF] |
| Vers l'élimination des contraintes d'étiquette dure dans les attaques d'inversion de gradient | CAS | ICLR | 2024 | [Pub] [Supp] [PDF] [Code] |
| Optimisation locale de point de selle composite locale | Université Purdue | ICLR | 2024 | [Pub] [PDF] |
| Amélioration de la formation neuronale via un modèle de dynamique corrélé | Dans | ICLR | 2024 | [Pub] [PDF] |
| EControl: Optimisation distribuée rapide avec compression et contrôle d'erreur | Université Saarland | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Construire des exemples contradictoires pour l'apprentissage fédéré vertical: corruption optimale du client via un bandit multi-armé | Hkust | ICLR | 2024 | [PUB] |
| Fedhyper: un planificateur de taux d'apprentissage universel et robuste pour l'apprentissage fédéré avec descente hypergradient | Umcp | ICLR | 2024 | [Pub] [Supp] [PDF] [Code] |
| Apprentissage fédéré personnalisé hétérogène par des mises à jour globales locales se mélangeant via le taux de convergence | Cuhk | ICLR | 2024 | [PUB] |
| Briser les bordures physiques et linguistiques: réglage rapide fédéré multilingue pour les langues à faible ressource | Université de Cambridge | ICLR | 2024 | [PUB] |
| Algorithme robuste byzantin simple minimax simple pour les objectifs non convexes avec une hétérogénéité du gradient uniforme | NTT Data Mathematical Systems Inc. | ICLR | 2024 | [PUB] |
| Vflair: une bibliothèque de recherche et une référence pour l'apprentissage fédéré vertical | JEU | ICLR | 2024 | [Pub] [PDF] [Code] |
| Apprentissage fédéré de l'incitation avec récompense du modèle de formation | Nus | ICLR | 2024 | [Pub] [Supp] |
| Vertibench: Avocation de la diversité de la distribution des fonctionnalités dans les références d'apprentissage fédérées verticales | 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 | Université de 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 | Université de Pittsburgh | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Federated Spectral Clustering via Secure Similarity Reconstruction | CUHK | NeurIPS | 2023 | [PUB] |
| Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM | UM-Dearborn | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks | CMU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Federated Multi-Objective Learning | RIT | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout | University of British Columbia; Gatech | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Resolving the Tug-of-War: A Separation of Communication and Learning in Federated Learning | Université de Pittsburgh | NeurIPS | 2023 | [PUB] [SUPP] |
| Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems | Université de Pittsburgh | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| StableFDG: Style and Attention Based Learning for Federated Domain Generalization | KAIST; Purdue University | NeurIPS | 2023 | [PUB] [PDF] |
| Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization | The University of 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 | PEU | 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 | WHU | 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 | Université de Pittsburgh | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Federated Learning via Meta-Variational Dropout | SNU | NeurIPS | 2023 | [PUB] [CODE] |
| Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates | Purdue University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| SPACE: Single-round Participant Amalgamation for Contribution Evaluation in Federated Learning | 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 | TROUSSE | 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 | Sofia University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| H-nobs: Achieving Certified Fairness and Robustness in Distributed Learning on Heterogeneous Datasets | GMU | NeurIPS | 2023 | [PUB] |
| Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking | Wyze Labs | NeurIPS Datasets and Benchmarks | 2023 | [PUB] [SUPP] [DATASET] |
| Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning | Google Research | NeurIPS Datasets and Benchmarks | 2023 | [PUB] [PDF] [DATASET] |
| Text-driven Prompt Generation for Vision-Language Models in Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| HePCo: Data-Free Heterogeneous Prompt Consolidation for Continual Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data | NeurIPS workshop | 2023 | [PUB] | |
| FedSoL: Bridging Global Alignment and Local Generality in Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| One-shot Empirical Privacy Estimation for Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning | NeurIPS workshop | 2023 | [PUB] | |
| SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models | NeurIPS workshop | 2023 | [PUB] | |
| The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| Towards Building the FederatedGPT: Federated Instruction Tuning | NeurIPS workshop | 2023 | [PUB] | |
| Federated Learning for Speech Recognition: Revisiting Current Trends Towards Large-Scale ASR | NeurIPS workshop | 2023 | [PUB] | |
| LASER: Linear Compression in Wireless Distributed Optimization | NeurIPS workshop | 2023 | [PUB] | |
| MARINA Meets Matrix Stepsizes: Variance Reduced Distributed Non-Convex Optimization | NeurIPS workshop | 2023 | [PUB] | |
| TAMUNA: Doubly Accelerated Federated Learning with Local Training, Compression, and Partial Participation | NeurIPS workshop | 2023 | [PUB] | |
| An Empirical Evaluation of Federated Contextual Bandit Algorithms | NeurIPS workshop | 2023 | [PUB] | |
| RealFM: A Realistic Mechanism to Incentivize Data Contribution and Device Participation | NeurIPS workshop | 2023 | [PUB] | |
| FDAPT: Federated Domain-adaptive Pre-training for Language Models | NeurIPS workshop | 2023 | [PUB] | |
| Making Batch Normalization Great in Federated Deep Learning | NeurIPS workshop | 2023 | [PUB] | |
| Correlated Noise Provably Beats Independent Noise for Differentially Private Learning | NeurIPS workshop | 2023 | [PUB] | |
| Parameter Averaging Laws for Multitask Language Models | NeurIPS workshop | 2023 | [PUB] | |
| Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages | NeurIPS workshop | 2023 | [PUB] | |
| Beyond Parameter Averaging in Model Aggregation | NeurIPS workshop | 2023 | [PUB] | |
| Augmenting Federated Learning with Pretrained Transformers | NeurIPS workshop | 2023 | [PUB] | |
| Consensus Optimization at Representation: Improving Personalized Federated Learning via Data-Centric Regularization | NeurIPS workshop | 2023 | [PUB] | |
| DPZero: Dimension-Independent and Differentially Private Zeroth-Order Optimization | NeurIPS workshop | 2023 | [PUB] | |
| Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System | NeurIPS workshop | 2023 | [PUB] | |
| Learning Optimizers for Local SGD | NeurIPS workshop | 2023 | [PUB] | |
| Exploring User-level Gradient Inversion with a Diffusion Prior | NeurIPS workshop | 2023 | [PUB] | |
| User Inference Attacks on Large Language Models | NeurIPS workshop | 2023 | [PUB] | |
| FedLDA: Personalized Federated Learning Through Collaborative Linear Discriminant Analysis | NeurIPS workshop | 2023 | [PUB] | |
| Heterogeneous LoRA for Federated Fine-tuning of On-device Foundation Models | NeurIPS workshop | 2023 | [PUB] | |
| Backdoor Threats from Compromised Foundation Models to Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| MOFL/D: A Federated Multi-objective Learning Framework with Decomposition | NeurIPS workshop | 2023 | [PUB] | |
| Absolute Variation Distance: an Inversion Attack Evaluation Metric for Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| Fed3R: Recursive Ridge Regression for Federated Learning with strong pre-trained models | NeurIPS workshop | 2023 | [PUB] | |
| FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| Private and Personalized Histogram Estimation in a Federated Setting | NeurIPS workshop | 2023 | [PUB] | |
| The Aggregation–Heterogeneity Trade-off in Federated Learning | PKU | POULAIN | 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 | The University of 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 | Groupe Alibaba | ICML | 2023 | [PUB] [PDF] [CODE] |
| Federated Conformal Predictors for Distributed Uncertainty Quantification | MIT | ICML | 2023 | [PUB] [PDF] [CODE] |
| Federated Adversarial Learning: A Framework with Convergence Analysis | UBC | ICML | 2023 | [PUB] [PDF] |
| Federated Heavy Hitter Recovery under Linear Sketching | Google Research | ICML | 2023 | [PUB] [PDF] [CODE] |
| Doubly Adversarial Federated Bandits | London School of Economics and Political Science | ICML | 2023 | [PUB] [PDF] [CODE] |
| Achieving Linear Speedup in Non-IID Federated Bilevel Learning | UC | ICML | 2023 | [PUB] [PDF] |
| One-Shot Federated Conformal Prediction | Université Paris-Saclay | ICML | 2023 | [PUB] [PDF] [CODE] |
| Federated Online and Bandit Convex Optimization | TTIC | ICML | 2023 | [PUB] |
| Federated Linear Contextual Bandits with User-level Differential Privacy | The Pennsylvania State University | ICML | 2023 | [PUB] [PDF] |
| Vertical Federated Graph Neural Network for Recommender System | NUS | ICML | 2023 | [PUB] [PDF] [CODE] |
| Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation | Université de 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 | Université d'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 | FRAPPER | 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 | Université Duke | ICML | 2023 | [PUB] [PDF] |
| Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design | Méta-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 | JEU | ICML | 2023 | [PUB] [PDF] |
| Efficient Personalized Federated Learning via Sparse Model-Adaptation | Groupe Alibaba | ICML | 2023 | [PUB] [PDF] [CODE] |
| From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning | Université. 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; Méta-IA | ICML | 2023 | [PUB] [PDF] |
| Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning | KU Louvain | 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 | Université de Chicago | ICML | 2023 | [PUB] [PDF] [CODE] |
| Ensemble and continual federated learning for classification tasks. | Universidade de Santiago de Compostela | Mach Learn | 2023 | [PUB] [PDF] |
| FAC-fed: Federated adaptation for fairness and concept drift aware stream classification | Leibniz University of Hannover | Mach Learn | 2023 | [PUB] |
| Robust federated learning under statistical heterogeneity via hessian-weighted aggregation | Deakin University | Mach Learn | 2023 | [PUB] |
| FedLab: A Flexible Federated Learning Framework | UESTC; Peng Cheng Lab | JMLR | 2023 | [PUB] [PDF] [CODE] |
| Minimax Estimation for Personalized Federated Learning: An Alternative between FedAvg and Local Training? | JMLR | 2023 | [PUB] | |
| Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning | TAMU | JMLR | 2023 | [PUB] [PDF] [CODE] |
| A First Look into the Carbon Footprint of Federated Learning | 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 | Hanyang University | TPAMI | 2023 | [PUB] [PDF] |
| Efficient Federated Learning Via Local Adaptive Amended Optimizer With Linear Speedup | University of Sydney | TPAMI | 2023 | [PDF] |
| Federated Learning Via Inexact ADMM. | BJTU | TPAMI | 2023 | [PUB] [PDF] [CODE] |
| FedIPR: Ownership Verification for Federated Deep Neural Network Models | SJTU | TPAMI | 2023 | [PUB] [PDF] [CODE] [解读] |
| Decentralized Federated Averaging | NUDT | TPAMI | 2023 | [PUB] [PDF] |
| Personalized Federated Learning with Feature Alignment and Classifier Collaboration | JEU | 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 | JEU | 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 | University of Sydney | ICLR | 2023 | [PUB] [CODE] |
| The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation | utexas | ICLR | 2023 | [PUB] [PDF] [CODE] |
| PerFedMask: Personalized Federated Learning with Optimized Masking Vectors | UBC | ICLR | 2023 | [PUB] [CODE] |
| EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data | GMU | ICLR | 2023 | [PUB] [CODE] |
| FedDAR: Federated Domain-Aware Representation Learning | Harvard | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning | upenn | ICLR | 2023 | [PUB] [CODE] |
| FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning | Purdue University | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses | RUC | ICLR | 2023 | [PUB] |
| Efficient Federated Domain Translation | Purdue University | ICLR | 2023 | [PUB] [CODE] |
| On the Importance and Applicability of Pre-Training for Federated Learning | OSU | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models | UMD | ICLR | 2023 | [PUB] [PDF] [CODE] |
| A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy | UCLA | ICLR | 2023 | [PUB] [PDF] |
| Instance-wise Batch Label Restoration via Gradients in Federated Learning | BUAA | ICLR | 2023 | [PUB] [CODE] |
| Data-Free One-Shot Federated Learning Under Very High Statistical Heterogeneity | College of William and Mary | ICLR | 2023 | [PUB] |
| CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning | 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. | Méta-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 | Hanyang University | TPAMI | 2022 | [PUB] |
| Lazily Aggregated Quantized Gradient Innovation for Communication-Efficient Federated Learning | ZJU | TPAMI | 2022 | [PUB] [CODE] |
| Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox | Moscow Institute of Physics and Technology | NeurIPS | 2022 | [PUB] [PDF] |
| LAMP: Extracting Text from Gradients with Language Model Priors | ETHZ | NeurIPS | 2022 | [PUB] [CODE] |
| FedAvg with Fine Tuning: Local Updates Lead to Representation Learning | utexas | NeurIPS | 2022 | [PUB] [PDF] |
| On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond | NUIST | NeurIPS | 2022 | [PUB] [PDF] |
| Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams | WISC | NeurIPS | 2022 | [PUB] [CODE] |
| Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks | Columbia University | 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 | JEU | 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? | WHU | NeurIPS | 2022 | [PUB] [CODE] |
| DENSE: Data-Free One-Shot Federated Learning | ZJU | NeurIPS | 2022 | [PUB] [PDF] |
| CalFAT: Calibrated Federated Adversarial Training with Label Skewness | ZJU | NeurIPS | 2022 | [PUB] [PDF] |
| SAGDA: Achieving O(ϵ−2) Communication Complexity in Federated Min-Max Learning | OSU | NeurIPS | 2022 | [PUB] [PDF] |
| Taming Fat-Tailed (“Heavier-Tailed” with Potentially Infinite Variance) Noise in Federated Learning | OSU | NeurIPS | 2022 | [PUB] [PDF] |
| Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness | PKU | NeurIPS | 2022 | [PUB] |
| Federated Submodel Optimization for Hot and Cold Data Features | SJTU | NeurIPS | 2022 | [PUB] |
| BooNTK: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels | 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 | Amazone | 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 | Université Duke | NeurIPS | 2022 | [PUB] [PDF] [CODE] |
| A Unified Analysis of Federated Learning with Arbitrary Client Participation | IBM | NeurIPS | 2022 | [PUB] [PDF] |
| Preservation of the Global Knowledge by Not-True Distillation in Federated Learning | KAIST | NeurIPS | 2022 | [PUB] [PDF] [CODE] |
| FedSR: A Simple and Effective Domain Generalization Method for Federated Learning | Université d'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 | Université de Pittsburgh | ICML | 2022 | [PUB] [PDF] [CODE] |
| Fast Composite Optimization and Statistical Recovery in Federated Learning | SJTU | ICML | 2022 | [PUB] [PDF] [CODE] |
| Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning | NYU | ICML | 2022 | [PUB] [PDF] [CODE] |
| The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning | Stanford; Google Research | ICML | 2022 | [PUB] [PDF] [CODE] [SLIDE] |
| The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation | Stanford; Google Research | ICML | 2022 | [PUB] [PDF] [CODE] |
| DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training | USTC | ICML | 2022 | [PUB] [PDF] [CODE] |
| FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning | University of Oulu | ICML | 2022 | [PUB] [PDF] [CODE] |
| DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning | University of Cambridge | ICML | 2022 | [PUB] [PDF] [SLIDE] [CODE] |
| Accelerated Federated Learning with Decoupled Adaptive Optimization | Université d'Auburn | ICML | 2022 | [PUB] [PDF] |
| Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling | Géorgie 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 | University of 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 | L'Université d'État de l'Ohio | ICML | 2022 | [PUB] [PDF] |
| QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning | Nankai University | ICML | 2022 | [PUB] [CODE] |
| Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization | KAIST | ICML | 2022 | [PUB] [PDF] |
| Neural Tangent Kernel Empowered Federated Learning | NC State University | ICML | 2022 | [PUB] [PDF] [CODE] |
| Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy | UMN | ICML | 2022 | [PUB] [PDF] |
| Personalized Federated Learning via Variational Bayesian Inference | CAS | ICML | 2022 | [PUB] [PDF] [SLIDE] [UC.] |
| Federated Learning with Label Distribution Skew via Logits Calibration | ZJU | ICML | 2022 | [PUB] |
| Neurotoxin: Durable Backdoors in Federated Learning | Southeast University;Princeton | ICML | 2022 | [PUB] [PDF] [CODE] |
| Resilient and Communication Efficient Learning for Heterogeneous Federated Systems | 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; University of Washington | ICLR | 2022 | [PUB] [PDF] |
| Acceleration of Federated Learning with Alleviated Forgetting in Local Training | JEU | 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; Université d'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 | L'Université d'État de l'Ohio | ICLR | 2022 | [PUB] [PDF] [CODE] |
| Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond | KAIST; MIT | ICLR | 2022 | [PUB] [PDF] |
| One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them. | JMLR | 2021 | [PUB] [CODE] | |
| Constrained differentially private federated learning for low-bandwidth devices | UAI | 2021 | [PUB] [PDF] | |
| Federated stochastic gradient Langevin dynamics | UAI | 2021 | [PUB] [PDF] | |
| Federated Learning Based on Dynamic Regularization | BU; BRAS | ICLR | 2021 | [PUB] [PDF] [CODE] |
| Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning | L'Université d'État de l'Ohio | ICLR | 2021 | [PUB] [PDF] |
| HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients | Université Duke | ICLR | 2021 | [PUB] [PDF] [CODE] |
| FedMix: Approximation of Mixup under Mean Augmented Federated Learning | KAIST | ICLR | 2021 | [PUB] [PDF] |
| Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms | CMU; Google | ICLR | 2021 | [PUB] [PDF] [CODE] |
| Adaptive Federated Optimization | ICLR | 2021 | [PUB] [PDF] [CODE] | |
| Personalized Federated Learning with First Order Model Optimization | Stanford; Nvidia | ICLR | 2021 | [PUB] [PDF] [CODE] [UC.] |
| FedBN: Federated Learning on Non-IID Features via Local Batch Normalization | Princeton | ICLR | 2021 | [PUB] [PDF] [CODE] |
| FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning | L'Université d'État de l'Ohio | ICLR | 2021 | [PUB] [PDF] [CODE] |
| Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning | KAIST | ICLR | 2021 | [PUB] [PDF] [CODE] |
| KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation | ZJU | ICML | 2021 | [PUB] [PDF] [CODE] [解读] |
| Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix | Université Harvard | ICML | 2021 | [PUB] [PDF] [VIDEO] [CODE] |
| FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis | PKU; Princeton | ICML | 2021 | [PUB] [PDF] [VIDEO] |
| Personalized Federated Learning using Hypernetworks | Bar-Ilan University; Nvidia | ICML | 2021 | [PUB] [PDF] [CODE] [PAGE] [VIDEO] [解读] |
| Federated Composite Optimization | Stanford; Google | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] [SLIDE] |
| Exploiting Shared Representations for Personalized Federated Learning | University of Texas at Austin; 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; Bras | 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; Amazone | 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 | Université d'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 | Google Research | NeurIPS | 2021 | [PUB] [PDF] [CODE] [UC.] |
| Fast Federated Learning in the Presence of Arbitrary Device Unavailability | JEU; 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 | JEU; 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; Google Research | NeurIPS | 2021 | [PUB] [CODE] [VIDEO] |
| Federated-EM with heterogeneity mitigation and variance reduction | Ecole Polytechnique; Google Research | NeurIPS | 2021 | [PUB] [PDF] |
| Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning | MIT; Amazone; 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; Amazone | 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 | The University of 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).
| Titre | Affiliation | Lieu | Année | Matériels |
|---|---|---|---|---|
| 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 | Université de Pittsburgh | KDD | 2023 | [PUB] [PDF] [CODE] |
| CriticalFL: A Critical Learning Periods Augmented Client Selection Framework for Efficient Federated Learning | SUNY-Binghamton University | KDD | 2023 | [PUB] [PDF] |
| FLAMES2Graph: An Interpretable Federated Multivariate Time Series Classification Framework | L3S Research Center | KDD | 2023 | [PUB] [PDF] |
| FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy | SJTU | KDD | 2023 | [PUB] [PDF] [CODE] |
| Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework | UCSD | KDD | 2023 | [PUB] [PDF] [CODE] |
| DM-PFL: Hitchhiking Generic Federated Learning for Efficient Shift-Robust Personalization | BUAA | KDD | 2023 | [PUB] [CODE] |
| FS-REAL: Towards Real-World Cross-Device Federated Learning | Groupe 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; Groupe 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 | JEU | 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 | Université de Pittsburgh | KDD | 2022 | [PUB] [PDF] |
| FLDetector: Detecting Malicious Clients in Federated Learning via Checking Model-Updates Consistency | USTC | KDD | 2022 | [PUB] [PDF] [CODE] |
| Practical Lossless Federated Singular Vector Decomposition Over Billion-Scale Data | HKUST | KDD | 2022 | [PUB] [PDF] [CODE] |
| FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy | SJTU | KDD | 2022 | [PUB] [PDF] |
| FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Platform for Federated Graph Learning | Alibaba | KDD (Best Paper Award) | 2022 | [PUB] [PDF] [CODE] |
| Fed-LTD: Towards Cross-Platform Ride Hailing via Federated Learning to Dispatch | BUAA | KDD | 2022 | [PUB] [PDF] [解读] |
| Felicitas: Federated Learning in Distributed Cross Device Collaborative Frameworks | USTC | KDD | 2022 | [PUB] [PDF] |
| No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices | Renmin University of China | KDD | 2022 | [PUB] [PDF] |
| FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling | JEU | 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 | Université de Nankin | 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 | Université Duke | KDD | 2021 | [PUB] [PDF] [CODE] |
| A Practical Federated Learning Framework for Small Number of Stakeholders | ETH Zürich | WSDM | 2021 | [PUB] [CODE] |
| Federated Deep Knowledge Tracing | USTC | WSDM | 2021 | [PUB] [CODE] |
| FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems | 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).
| Titre | Affiliation | Lieu | Année | Matériels |
|---|---|---|---|---|
| Byzantine-Robust Decentralized Federated Learning | CSC | 2024 | [PUB] | |
| Not One Less: Exploring Interplay between User Profiles and Items in Untargeted Attacks against Federated Recommendation | CSC | 2024 | [PUB] | |
| Cross-silo Federated Learning with Record-level Personalized Differential Privacy. | CSC | 2024 | [PUB] | |
| Samplable Anonymous Aggregation for Private Federated Data Analysis | CSC | 2024 | [PUB] | |
| Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential Privacy | CSC | 2024 | [PUB] | |
| Distributed Backdoor Attacks on Federated Graph Learning and Certified Defenses | CSC | 2024 | [PUB] | |
| Two-Tier Data Packing in RLWE-based Homomorphic Encryption for Secure Federated Learning. | CSC | 2024 | [PUB] | |
| Poster: Protection against Source Inference Attacks in Federated Learning using Unary Encoding and Shuffling. | CSC | 2024 | [PUB] | |
| Poster: End-to-End Privacy-Preserving Vertical Federated Learning using Private Cross-Organizational Data Collaboration. | CSC | 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 | CSC | 2023 | [PUB] [PDF] |
| MESAS: Poisoning Defense for Federated Learning Resilient against Adaptive Attackers | University of Würzburg | CSC | 2023 | [PUB] |
| martFL: Enabling Utility-Driven Data Marketplace with a Robust and Verifiable Federated Learning Architecture | JEU | CSC | 2023 | [PUB] [PDF] [CODE] |
| Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks | UIUC | CSC | 2023 | [PUB] [PDF] |
| Poster: Verifiable Data Valuation with Strong Fairness in Horizontal Federated Learning | NSYSU | CSC | 2023 | [PUB] |
| Poster: Bridging Trust Gaps: Data Usage Transparency in Federated Data Ecosystems | Université RWTH d'Aix-la-Chapelle | CSC | 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 | CSC | 2023 | [PUB] [PDF] [CODE] |
| CERBERUS: Exploring Federated Prediction of Security Events | UCL London | CSC | 2022 | [PUB] [PDF] |
| EIFFeL: Ensuring Integrity for Federated Learning | UW-Madison | CSC | 2022 | [PUB] [PDF] |
| Eluding Secure Aggregation in Federated Learning via Model Inconsistency | SPRING Lab; EPFL | CSC | 2022 | [PUB] [PDF] [CODE] |
| Federated Boosted Decision Trees with Differential Privacy | University of Warwick | CSC | 2022 | [PUB] [PDF] [CODE] |
| FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information | Université Duke | S&P | 2023 | [PUB] [PDF] |
| Scalable and Privacy-Preserving Federated Principal Component Analysis | EPFL; Tune Insight SA | S&P | 2023 | [PUB] [PDF] |
| SafeFL: MPC-friendly Framework for Private and Robust Federated Learning | TU Darmstadt | S&P Workshop | 2023 | [PUB] |
| On the Pitfalls of Security Evaluation of Robust Federated Learning. | umass | S&P Workshop | 2023 | [PUB] |
| BayBFed: Bayesian Backdoor Defense for Federated Learning | TU Darmstadt; UTSA | S&P | 2023 | [PUB] [PDF] |
| 3DFed: Adaptive and Extensible Framework for Covert Backdoor Attack in Federated Learning | PolyU | S&P | 2023 | [PUB] [CODE] |
| RoFL: Robustness of Secure Federated Learning. | ETH 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 | Université Fudan | S&P | 2023 | [PUB] [PDF] |
| Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning | University of Massachusetts | S&P | 2022 | [PUB] [VIDEO] |
| SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost | 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 | CSC | 2021 | [PUB] [PDF] |
| FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping | Université Duke | NDSS | 2021 | [PUB] [PDF] [CODE] [VIDEO] [SLIDE] |
| POSEIDON: Privacy-Preserving Federated Neural Network Learning | EPFL | NDSS | 2021 | [PUB] [VIDEO] |
| Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning | 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 | L'Université d'État de l'Ohio | USENIX Security | 2020 | [PUB] [PDF] [CODE] [VIDEO] [SLIDE] |
| A Reliable and Accountable Privacy-Preserving Federated Learning Framework using the Blockchain | Université du 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 | CSC | 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).
| Titre | Affiliation | Lieu | Année | Matériels |
|---|---|---|---|---|
| DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations | MM | 2024 | [PUB] | |
| One-shot-but-not-degraded Federated Learning | MM | 2024 | [PUB] | |
| Overcoming Spatial-Temporal Catastrophic Forgetting for Federated Class-Incremental Learning | MM | 2024 | [PUB] | |
| FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models | MM | 2024 | [PUB] | |
| Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-rank Decomposition | MM | 2024 | [PUB] | |
| CoAst: Validation-Free Contribution Assessment for Federated Learning based on Cross-Round Valuation | MM | 2024 | [PUB] | |
| Spatio-temporal Heterogeneous Federated Learning for Time Series Classification with Multi-view Orthogonal Training | MM | 2024 | [PUB] | |
| FedEvalFair: A Privacy-Preserving and Statistically Grounded Federated Fairness Evaluation Framework | MM | 2024 | [PUB] | |
| One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity | MM | 2024 | [PUB] | |
| FedSLS: Exploring Federated Aggregation in Saliency Latent Space | MM | 2024 | [PUB] | |
| Cluster-driven Personalized Federated Recommendation with Interest-aware Graph Convolution Network for Multimedia | MM | 2024 | [PUB] | |
| FedBCGD: Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning | MM | 2024 | [PUB] | |
| Federated Morozov Regularization for Shortcut Learning in Privacy Preserving Learning with Watermarked Image Data | MM | 2024 | [PUB] | |
| Cross-Modal Meta Consensus for Heterogeneous Federated Learning | MM | 2024 | [PUB] | |
| Masked Random Noise for Communication-Efficient Federated Learning | MM | 2024 | [PUB] | |
| Heterogeneity-Aware Federated Deep Multi-View Clustering towards Diverse Feature Representations | MM | 2024 | [PUB] | |
| Adaptive Hierarchical Aggregation for Federated Object Detection | MM | 2024 | [PUB] | |
| FedCAFE: Federated Cross-Modal Hashing with Adaptive Feature Enhancement | MM | 2024 | [PUB] | |
| Federated Fuzzy C-means with Schatten-p Norm Minimization | MM | 2024 | [PUB] | |
| Towards Effective Federated Graph Anomaly Detection via Self-boosted Knowledge Distillation | MM | 2024 | [PUB] | |
| Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification | IJCV | 2024 | [PUB] | |
| FedHide: Federated Learning by Hiding in the Neighbors | ECCV | 2024 | [PUB] | |
| FedVAD: Enhancing Federated Video Anomaly Detection with GPT-Driven Semantic Distillation | ECCV | 2024 | [PUB] | |
| FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients | ECCV | 2024 | [PUB] | |
| Pick-a-Back: Selective Device-to-Device Knowledge Transfer in Federated Continual Learning | ECCV | 2024 | [PUB] | |
| Federated Learning with Local Openset Noisy Labels | ECCV | 2024 | [PUB] | |
| FedTSA: A Cluster-Based Two-Stage Aggregation Method for Model-Heterogeneous Federated Learning. | ECCV | 2024 | [PUB] | |
| Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality Selection | ECCV | 2024 | [PUB] | |
| BAFFLE: A Baseline of Backpropagation-Free Federated Learning | ECCV | 2024 | [PUB] | |
| PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learning | ECCV | 2024 | [PUB] | |
| Fisher Calibration for Backdoor-Robust Heterogeneous Federated Learning | ECCV | 2024 | [PUB] | |
| Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents | ECCV | 2024 | [PUB] | |
| FedHARM: Harmonizing Model Architectural Diversity in Federated Learning | ECCV | 2024 | [PUB] | |
| SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-device Inference. | ECCV | 2024 | [PUB] | |
| Personalized Federated Domain-Incremental Learning Based on Adaptive Knowledge Matching. | ECCV | 2024 | [PUB] | |
| Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning | ECCV | 2024 | [PUB] | |
| Towards Multi-modal Transformers in Federated Learning | ECCV | 2024 | [PUB] | |
| Local and Global Flatness for Federated Domain Generalization | ECCV | 2024 | [PUB] | |
| Feature Diversification and Adaptation for Federated Domain Generalization | ECCV | 2024 | [PUB] | |
| PFEDEDIT: Personalized Federated Learning via Automated Model Editing | ECCV | 2024 | [PUB] | |
| FedHCA2: Towards Hetero-Client Federated Multi-Task Learning | SJTU | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity | WHU | 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 | Utah | 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 | ND | 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 | WHU | 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 | MM | 2023 | [PUB] |
| FedVQA: Personalized Federated Visual Question Answering over Heterogeneous Scenes | Leiden University | MM | 2023 | [PUB] |
| Towards Fast and Stable Federated Learning: Confronting Heterogeneity via Knowledge Anchor | XJTU | MM | 2023 | [PUB] [PDF] [CODE] |
| Federated Deep Multi-View Clustering with Global Self-Supervision | UESTC | MM | 2023 | [PUB] [PDF] |
| FedAA: Using Non-sensitive Modalities to Improve Federated Learning while Preserving Image Privacy | ZJU | MM | 2023 | [PUB] |
| Prototype-guided Knowledge Transfer for Federated Unsupervised Cross-modal Hashing | SDNU | MM | 2023 | [PUB] [CODE] |
| Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data | ZJU | MM | 2023 | [PUB] [PDF] |
| FedCD: A Classifier Debiased Federated Learning Framework for Non-IID Data | BUPT | MM | 2023 | [PUB] |
| Federated Learning with Label-Masking Distillation | UCAS | MM | 2023 | [PUB] [CODE] |
| Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data | SDU | MM | 2023 | [PUB] [PDF] [CODE] |
| A Four-Pronged Defense Against Byzantine Attacks in Federated Learning | HUST | MM | 2023 | [PUB] [PDF] |
| Client-Adaptive Cross-Model Reconstruction Network for Modality-Incomplete Multimodal Federated Learning | CAS; Peng Cheng Laboratory; UCAS | MM | 2023 | [PUB] |
| FedGH: Heterogeneous Federated Learning with Generalized Global Header | NKU | MM | 2023 | [PUB] [PDF] [CODE] |
| Cuing Without Sharing: A Federated Cued Speech Recognition Framework via Mutual Knowledge Distillation | CUHK | MM | 2023 | [PUB] [PDF] [CODE] |
| AffectFAL: Federated Active Affective Computing with Non-IID Data | TJUT | MM | 2023 | [PUB] [CODE] |
| Improving Federated Person Re-Identification through Feature-Aware Proximity and Aggregation | SZU | MM | 2023 | [PUB] |
| Towards Attack-tolerant Federated Learning via Critical Parameter Analysis | KAIST | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generation | NTU; Nvidia | ICCV | 2023 | [PUB] [PDF] [SUPP] |
| Generative Gradient Inversion via Over-Parameterized Networks in Federated Learning | A*STAR | ICCV | 2023 | [PUB] [CODE] [SUPP] |
| GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning | SJTU | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| Workie-Talkie: Accelerating Federated Learning by Overlapping Computing and Communications via Contrastive Regularization | University of Houston | ICCV | 2023 | [PUB] [SUPP] |
| PGFed: Personalize Each Client's Global Objective for Federated Learning | Université de Pittsburgh | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learning | UCF | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning | TCL AI Lab | ICCV | 2023 | [PUB] [PDF] [SUPP] |
| FedPD: Federated Open Set Recognition with Parameter Disentanglement | City University of Hong Kong | ICCV | 2023 | [PUB] [CODE] |
| TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation | ETH Zurich; Sony AI | ICCV | 2023 | [PUB] [PDF] [CODE] |
| Towards Instance-adaptive Inference for Federated Learning | A*STAR | ICCV | 2023 | [PUB] [PDF] [CODE] |
| Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence | SCU; Engineering Research Center of Machine Learning and Industry Intelligence | ICCV | 2023 | [PUB] [PDF] [CODE] |
| zPROBE: Zero Peek Robustness Checks for Federated Learning | Purdue University | ICCV | 2023 | [PUB] [PDF] [SUPP] |
| ProtoFL: Unsupervised Federated Learning via Prototypical Distillation | KakaoBank Corp. | ICCV | 2023 | [PUB] [PDF] |
| MAS: Towards Resource-Efficient Federated Multiple-Task Learning | Sony AI | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation | PKU | ICCV | 2023 | [PUB] [PDF] [SUPP] |
| When Do Curricula Work in Federated Learning? | UCSD | ICCV | 2023 | [PUB] [PDF] [SUPP] |
| Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples | Université Duke | ICCV | 2023 | [PUB] [PDF] [CODE] |
| Multi-Metrics Adaptively Identifies Backdoors in Federated Learning | QUEUE | 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 | The University of 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 | WHU | 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 | WHU | 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 | JEU | CVPR | 2023 | [PUB] [PDF] [CODE] |
| Confidence-Aware Personalized Federated Learning via Variational Expectation Maximization | KU Louvain | 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 | MM | 2022 | [PUB] |
| Few-Shot Model Agnostic Federated Learning | WHU | MM | 2022 | [PUB] [CODE] |
| Feeling Without Sharing: A Federated Video Emotion Recognition Framework Via Privacy-Agnostic Hybrid Aggregation | TJUT | MM | 2022 | [PUB] |
| FedLTN: Federated Learning for Sparse and Personalized Lottery Ticket Networks | ECCV | 2022 | [PUB] [SUPP] | |
| Auto-FedRL: Federated Hyperparameter Optimization for Multi-Institutional Medical Image Segmentation | ECCV | 2022 | [PUB] [SUPP] [PDF] [CODE] | |
| Improving Generalization in Federated Learning by Seeking Flat Minima | Politecnico di Torino | ECCV | 2022 | [PUB] [SUPP] [PDF] [CODE] |
| AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation | ECCV | 2022 | [PUB] [SUPP] [PDF] [CODE] [PAGE] | |
| SphereFed: Hyperspherical Federated Learning | ECCV | 2022 | [PUB] [SUPP] [PDF] | |
| Federated Self-Supervised Learning for Video Understanding | ECCV | 2022 | [PUB] [PDF] [CODE] | |
| FedVLN: Privacy-Preserving Federated Vision-and-Language Navigation | ECCV | 2022 | [PUB] [SUPP] [PDF] [CODE] | |
| Addressing Heterogeneity in Federated Learning via Distributional Transformation | ECCV | 2022 | [PUB] [CODE] | |
| FedX: Unsupervised Federated Learning with Cross Knowledge Distillation | KAIST | ECCV | 2022 | [PUB] [SUPP] [PDF] [CODE] |
| Personalizing Federated Medical Image Segmentation via Local Calibration | Xiamen University | ECCV | 2022 | [PUB] [SUPP] [PDF] [CODE] |
| ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework | FRAPPER | 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 | Université Duke | CVPR | 2022 | [PUB] [SUPP] [PDF] |
| Layer-Wised Model Aggregation for Personalized Federated Learning | PolyU | CVPR | 2022 | [PUB] [SUPP] [PDF] |
| Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning | University of Central Florida | CVPR | 2022 | [PUB] [SUPP] [PDF] [CODE] |
| Federated Learning With Position-Aware Neurons | Université de Nankin | 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 | Université de Wuhan | CVPR | 2022 | [PUB] [CODE] [VIDEO] |
| Robust Federated Learning With Noisy and Heterogeneous Clients | Université de Wuhan | CVPR | 2022 | [PUB] [SUPP] [CODE] |
| ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning | Université d'État de l'Arizona | CVPR | 2022 | [PUB] [SUPP] [PDF] [CODE] |
| FedDC: Federated Learning With Non-IID Data via Local Drift Decoupling and Correction | National University of Defense Technology | CVPR | 2022 | [PUB] [PDF] [CODE] [解读] |
| Federated Class-Incremental Learning | CAS; Northwestern University; UTS | CVPR | 2022 | [PUB] [PDF] [CODE] |
| Fine-Tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning | PKU; JD Explore Academy; The University of 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; Google Research | CVPR | 2022 | [PUB] [PDF] [CODE] [VIDEO] |
| CD2-pFed: Cyclic Distillation-Guided Channel Decoupling for Model Personalization in Federated Learning | SJTU | CVPR | 2022 | [PUB] [PDF] |
| Closing the Generalization Gap of Cross-Silo Federated Medical Image Segmentation | Université. 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 | Université Duke | CVPR workshop | 2022 | [PUB] [PDF] [SILDES] [VIDEO] |
| Communication-Efficient Federated Data Augmentation on Non-IID Data | UESTC | CVPR workshop | 2022 | [PUB] |
| Does Federated Dropout Actually Work? | Stanford | CVPR workshop | 2022 | [PUB] [VIDEO] |
| FedIris: Towards More Accurate and Privacy-preserving Iris Recognition via Federated Template Communication | USTC; CRIPAC; CASIA | CVPR workshop | 2022 | [PUB] [SLIDES] [VIDEO] |
| Multi-Institutional Collaborations for Improving Deep Learning-Based Magnetic Resonance Image Reconstruction Using Federated Learning | Université Johns Hopkins | 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 | Université Duke | CVPR | 2021 | [PUB] [PDF] [CODE] |
| Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment | PKU | ICCV | 2021 | [PUB] |
| Ensemble Attention Distillation for Privacy-Preserving Federated Learning | Université de 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 | MM | 2021 | [PUB] [PDF] |
| Federated Visual Classification with Real-World Data Distribution | MIT; Google | ECCV | 2020 | [PUB] [PDF] [VIDEO] |
| InvisibleFL: Federated Learning over Non-Informative Intermediate Updates against Multimedia Privacy Leakages | MM | 2020 | [PUB] | |
Performance Optimization of Federated Person Re-identification via Benchmark Analysis data. | NTU | MM | 2020 | [PUB] [PDF] [CODE] [解读] |
Federated Learning papers accepted by top AI and NLP conference and journal, including ACL(Annual Meeting of the Association for Computational Linguistics), NAACL(North American Chapter of the Association for Computational Linguistics), EMNLP(Conference on Empirical Methods in Natural Language Processing) and COLING(International Conference on Computational Linguistics).
| Titre | Affiliation | Lieu | Année | Matériels |
|---|---|---|---|---|
| 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 | Université d'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 | OSU | Liste de contrôle d'accès | 2023 | [PUB] [PDF] [CODE] |
| FEDLEGAL: The First Real-World Federated Learning Benchmark for Legal NLP | FRAPPER; Peng Cheng Lab | Liste de contrôle d'accès | 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 | FRAPPER; Peng Cheng Lab | EMNLP | 2022 | [PUB] [CODE] |
| Fair NLP Models with Differentially Private Text Encoders | Université. 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 | Amazone | 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; Amazone | NAACL | 2022 | [PUB] [PDF] |
| Training Mixed-Domain Translation Models via Federated Learning | Amazone | NAACL | 2022 | [PUB] [PAGE] [PDF] |
| Pretrained Models for Multilingual Federated Learning | Université Johns Hopkins | NAACL | 2022 | [PUB] [PDF] [CODE] |
| Federated Chinese Word Segmentation with Global Character Associations | University of 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 | University of 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; Amazone | 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).
| Titre | Affiliation | Lieu | Année | Matériels |
|---|---|---|---|---|
| ReFer: Retrieval-Enhanced Vertical Federated Recommendation for Full Set User Benefit | JEU | 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 | Groupe 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 | Groupe 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 | Université technique de Munich | SIGIR | 2021 | [PUB] |
| FedCMR: Federated Cross-Modal Retrieval. | Dalian University of Technology | SIGIR | 2021 | [PUB] [CODE] |
| Meta Matrix Factorization for Federated Rating Predictions. | SDU | SIGIR | 2020 | [PUB] [PDF] |
Federated Learning papers accepted by top Database conference and journal, including SIGMOD(ACM SIGMOD Conference) , ICDE(IEEE International Conference on Data Engineering) and VLDB(Very Large Data Bases Conference).
| Titre | Affiliation | Lieu | Année | Matériels |
|---|---|---|---|---|
| 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 | Groupe 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 | Columbia University | ICDE | 2023 | [PUB] [CODE] |
| FedKNOW: Federated Continual Learning with Signature Task Knowledge Integration at Edge | PEU | 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. | PEU | VLDB | 2023 | [PUB] [CODE] |
| FS-Real: A Real-World Cross-Device Federated Learning Platform. | Groupe Alibaba | VLDB | 2023 | [PUB] [PDF] [CODE] |
| Federated Calibration and Evaluation of Binary Classifiers. | méta | 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. | FRAPPER | 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 | Université. 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 | Université Simon Fraser | VLDB | 2021 | [PUB] [PDF] [CODE] |
| Refiner: A Reliable Incentive-Driven Federated Learning System Powered by Blockchain | ZJU | VLDB | 2021 | [PUB] |
| Tanium Reveal: A Federated Search Engine for Querying Unstructured File Data on Large Enterprise Networks | Tanium Inc. | VLDB | 2021 | [PUB] [VIDEO] |
| VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning | PKU | SIGMOD | 2021 | [PUB] |
| ExDRa: Exploratory Data Science on Federated Raw Data | SIEMENS | SIGMOD | 2021 | [PUB] |
| Joint blockchain and federated learning-based offloading in harsh edge computing environments | TJU | SIGMOD workshop | 2021 | [PUB] |
| Privacy Preserving Vertical Federated Learning for Tree-based Models | 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).
| Titre | Affiliation | Lieu | Année | Matériels |
|---|---|---|---|---|
| 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; CUP | 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. | Emory University | 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 Louvain | WWW (Companion Volume) | 2023 | [PUB] |
| Privacy-Preserving Online Content Moderation: A Federated Learning Use Case. | COUPER | WWW (Companion Volume) | 2023 | [PUB] [PDF] |
| Privacy-Preserving Online Content Moderation with Federated Learning. | COUPER | 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. | Université de 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 | JEU | 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 | Université de technologie du Guangdong | INFOCOM | 2023 | [PUB] [PDF] |
| AOCC-FL: Federated Learning with Aligned Overlapping via Calibrated Compensation | HUST | INFOCOM | 2023 | [PUB] |
| Asynchronous Federated Unlearning | Université de 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 | The University of 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 | Université du Nord-Ouest | INFOCOM | 2023 | [PUB] |
| More than Enough is Too Much: Adaptive Defenses against Gradient Leakage in Production Federated Learning | Université de Toronto | INFOCOM | 2023 | [PUB] [PDF] [WEIBO] |
| Network Adaptive Federated Learning: Congestion and Lossy Compression | UTAustin | INFOCOM | 2023 | [PUB] [PDF] |
| OBLIVION: Poisoning Federated Learning by Inducing Catastrophic Forgetting | The Hang Seng University of Hong Kong | INFOCOM | 2023 | [PUB] [CODE] |
| Privacy as a Resource in Differentially Private Federated Learning | BUPT | INFOCOM | 2023 | [PUB] |
| SplitGP: Achieving Both Generalization and Personalization in Federated Learning | KAIST | INFOCOM | 2023 | [PUB] [PDF] |
| SVDFed: Enabling Communication-Efficient Federated Learning via Singular-Value-Decomposition | Beihang University | INFOCOM | 2023 | [PUB] |
| Tackling System Induced Bias in Federated Learning: Stratification and Convergence Analysis | Université des sciences et technologies du Sud | 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 | Université d'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 | Université de Toronto | INFOCOM | 2022 | [PUB] |
| Optimal Rate Adaption in Federated Learning with Compressed Communications | SZU | INFOCOM | 2022 | [PUB] [PDF] |
| The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining. | CityU | INFOCOM | 2022 | [PUB] [PDF] |
| Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling. | CUHK; AIRS ;Yale University | INFOCOM | 2022 | [PUB] [PDF] |
| Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization | Army Research Laboratory, Adelphi | INFOCOM | 2022 | [PUB] [PDF] |
| FLASH: Federated Learning for Automated Selection of High-band mmWave Sectors | 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 | Université de Californie | SIGMETRICS | 2021 | [PUB] [PDF] |
| Hermes: an efficient federated learning framework for heterogeneous mobile clients | Université Duke | MobiCom | 2021 | [PUB] |
| Federated mobile sensing for activity recognition | Samsung AI Center | MobiCom | 2021 | [PUB] [PAGE] [TALKS] [VIDEO] |
| Learning for Learning: Predictive Online Control of Federated Learning with Edge Provisioning. | Université de Nankin | 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 | JEU | 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 | Université d'État de l'Arizona | INFOCOM | 2021 | [PUB] [PDF] |
| Dual Attention-Based Federated Learning for Wireless Traffic Prediction | King Abdullah University of Science and Technology | INFOCOM | 2021 | [PUB] [PDF] [CODE] |
| FedSens: A Federated Learning Approach for Smart Health Sensing with Class Imbalance in Resource Constrained Edge Computing | Université de 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. | Université de Nankin | INFOCOM | 2020 | [PUB] |
| Optimizing Federated Learning on Non-IID Data with Reinforcement Learning | Université de Toronto | INFOCOM | 2020 | [PUB] [SLIDE] [CODE] [解读] |
| Enabling Execution Assurance of Federated Learning at Untrusted Participants | JEU | 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 | The University of Sydney | INFOCOM | 2019 | [PUB] [CODE] |
| Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning | Université 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).
| Titre | Affiliation | Lieu | Année | Matériels |
|---|---|---|---|---|
| AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems. | CAD | 2024 | [PUB] | |
| Fake Node-Based Perception Poisoning Attacks against Federated Object Detection Learning in Mobile Computing Networks | CAD | 2024 | [PUB] | |
| Flagger: Cooperative Acceleration for Large-Scale Cross-Silo Federated Learning Aggregation | ISCA | 2024 | [PUB] | |
| FedTrans: Efficient Federated Learning via Multi-Model Transformation | UIUC | MLSys | 2024 | [PUB] [PDF] |
| LIFL: A Lightweight, Event-driven Serverless Platform for Federated Learning | UC Riverside | MLSys | 2024 | [PUB] [PDF] |
| HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning | 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 | Virginia Tech | EuroSys | 2024 | [PUB] [CODE] |
| Totoro: A Scalable Federated Learning Engine for the Edge | UCSC | EuroSys | 2024 | [PUB] |
| Dordis: Efficient Federated Learning with Dropout-Resilient Differential Privacy | HKUST | EuroSys | 2024 | [PUB] [PDF] [CODE] |
| FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN | EuroSys workshop | 2024 | [PUB] | |
| ALS Algorithm for Robust and Communication-Efficient Federated Learning | EuroSys workshop | 2024 | [PUB] | |
| FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission. | EuroSys workshop | 2024 | [PUB] | |
| Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting. | TPDS | 2024 | [PUB] | |
| SR-FDIL: Synergistic Replay for Federated Domain-Incremental Learning | TPDS | 2024 | [PUB] | |
| FedVeca: Federated Vectorized Averaging on Non-IID Data With Adaptive Bi-Directional Global Objective | TPDS | 2024 | [PUB] | |
| Trusted Model Aggregation With Zero-Knowledge Proofs in Federated Learning. | TPDS | 2024 | [PUB] | |
| Accelerating Communication-Efficient Federated Multi-Task Learning With Personalization and Fairness. | TPDS | 2024 | [PUB] | |
| Privacy-Preserving Data Selection for Horizontal and Vertical Federated Learning. | TPDS | 2024 | [PUB] | |
| High-Performance Hardware Acceleration Architecture for Cross-Silo Federated Learning | TPDS | 2024 | [PUB] | |
| Joint Participant and Learning Topology Selection for Federated Learning in Edge Clouds | TPDS | 2024 | [PUB] | |
| Synchronize Only the Immature Parameters: Communication-Efficient Federated Learning By Freezing Parameters Adaptively | SJTU | TPDS | 2024 | [PUB] |
| FedREM: Guided Federated Learning in the Presence of Dynamic Device Unpredictability | SYSU | TPDS | 2024 | [PUB] |
| Fed-RAC: Resource-Aware Clustering for Tackling Heterogeneity of Participants in Federated Learning | IITP | TPDS | 2024 | [PUB] [PDF] |
| Taking Advantage of the Mistakes: Rethinking Clustered Federated Learning for IoT Anomaly Detection | UVIC | TPDS | 2024 | [PUB] |
| FedICT: Federated Multi-Task Distillation for Multi-Access Edge Computing | UCAS | TPDS | 2024 | [PUB] [PDF] |
| Collaboration in Federated Learning With Differential Privacy: A Stackelberg Game Analysis | SYSU | TPDS | 2024 | [PUB] |
| FAST: Enhancing Federated Learning Through Adaptive Data Sampling and Local Training | USTC | TPDS | 2024 | [PUB] |
| EcoFed: Efficient Communication for DNN Partitioning-Based Federated Learning | University of St Andrews | TPDS | 2024 | [PUB] [PDF] [CODE] |
| FedHAP: Federated Hashing With Global Prototypes for Cross-Silo Retrieval | JEU | 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 | FRAPPER | TCAD | 2024 | [PUB] |
| BSR-FL: An Efficient Byzantine-Robust Privacy-Preserving Federated Learning Framework | CT | 2024 | [PUB] | |
| User-Distribution-Aware Federated Learning for Efficient Communication and Fast Inference | ECNU; SHU | CT | 2024 | [PUB] |
| FedRFQ: Prototype-Based Federated Learning With Reduced Redundancy, Minimal Failure, and Enhanced Quality | SDU | CT | 2024 | [PUB] [PDF] |
| Value of Information: A Comprehensive Metric for Client Selection in Federated Edge Learning | SDU | CT | 2024 | [PUB] |
| Age-Aware Data Selection and Aggregator Placement for Timely Federated Continual Learning in Mobile Edge Computing | DLUT | CT | 2024 | [PUB] |
| FedGKD: Toward Heterogeneous Federated Learning via Global Knowledge Distillation | HUST | CT | 2024 | [PUB] [PDF] |
| Digital Twin-Assisted Federated Learning Service Provisioning Over Mobile Edge Networks | SDU | CT | 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 | Université d'Exeter | CT | 2023 | [PUB] |
| Towards Data-Independent Knowledge Transfer in Model-Heterogeneous Federated Learning | PolyU | CT | 2023 | [PUB] |
| A New Federated Scheduling Algorithm for Arbitrary-Deadline DAG Tasks | NEFU | CT | 2023 | [PUB] |
| Privacy-Enhanced Decentralized Federated Learning at Dynamic Edge | SDU | CT | 2023 | [PUB] |
| Byzantine-Resilient Federated Learning at Edge | SDU | CT | 2023 | [PUB] [PDF] |
| PrivAim: A Dual-Privacy Preserving and Quality-Aware Incentive Mechanism for Federated Learning | CSU | CT | 2023 | [PUB] |
| Accelerating Federated Learning With a Global Biased Optimiser | Université d'Exeter | CT | 2023 | [PUB] [PDF] [CODE] |
| Type-Aware Federated Scheduling for Typed DAG Tasks on Heterogeneous Multicore Platforms | TU Dortmund University | CT | 2023 | [PUB] [CODE] |
| Sandbox Computing: A Data Privacy Trusted Sharing Paradigm Via Blockchain and Federated Learning. | BUPT | CT | 2023 | [PUB] |
| CHEESE: Distributed Clustering-Based Hybrid Federated Split Learning Over Edge Networks | SUDA | TPDS | 2023 | [PUB] |
| Hierarchical Federated Learning With Momentum Acceleration in Multi-Tier Networks | University of Sydney | TPDS | 2023 | [PUB] [PDF] |
| Dap-FL: Federated Learning Flourishes by Adaptive Tuning and Secure Aggregation | Xidian University | TPDS | 2023 | [PUB] [PDF] [CODE] |
| Collaborative Intrusion Detection System for SDVN: A Fairness Federated Deep Learning Approach | 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 | Université d'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 | Université d'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 | FRAPPER | 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. | FRAPPER | 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 | CT | 2022 | [PUB] [CODE] | |
| L4L: Experience-Driven Computational Resource Control in Federated Learning | CT | 2022 | [PUB] | |
| Adaptive Federated Learning on Non-IID Data With Resource Constraint | CT | 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 | Université de Californie à San Diego | CAD | 2022 | [PUB] |
| A Decentralized Federated Learning Framework via Committee Mechanism With Convergence Guarantee | SYSU | TPDS | 2022 | [PUB] [PDF] |
| Improving Federated Learning With Quality-Aware User Incentive and Auto-Weighted Model Aggregation | JEU | 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 | PEU | TPDS | 2022 | [PUB] |
| Federated Learning With Nesterov Accelerated Gradient | The University of 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. | JEU | TPDS | 2022 | [PUB] |
| DONE: Distributed Approximate Newton-type Method for Federated Edge Learning. | University of Sydney | TPDS | 2022 | [PUB] [PDF] [CODE] |
| Flexible Clustered Federated Learning for Client-Level Data Distribution Shift. | CQU | TPDS | 2022 | [PUB] [PDF] [CODE] |
| Min-Max Cost Optimization for Efficient Hierarchical Federated Learning in Wireless Edge Networks. | Xidian University | TPDS | 2022 | [PUB] |
| LightFed: An Efficient and Secure Federated Edge Learning System on Model Splitting. | CSU | TPDS | 2022 | [PUB] |
| On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Federated Learning. | Purdue | TPDS | 2022 | [PUB] [PDF] [CODE] |
| Incentive-Aware Autonomous Client Participation in Federated Learning. | Université Sun Yat Sen | 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. | Université d'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. | Méta-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 | CT | 2021 | [PDF] [PUB] [CODE] |
| Efficient Federated Learning for Cloud-Based AIoT Applications | ECNU | TCAD | 2021 | [PUB] |
| HADFL: Heterogeneity-aware Decentralized Federated Learning Framework | USTC | CAD | 2021 | [PDF] [PUB] |
| Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration. | GMU | CAD | 2021 | [PDF] [PUB] |
| FedLight: Federated Reinforcement Learning for Autonomous Multi-Intersection Traffic Signal Control. | ECNU | CAD | 2021 | [PUB] |
| Oort: Efficient Federated Learning via Guided Participant Selection | 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 | QUEUE | 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).
| Titre | Affiliation | Lieu | Année | Matériels |
|---|---|---|---|---|
| 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. | Virginia Tech | 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.
| Titre | Affiliation | Lieu | Année | Matériels |
|---|---|---|---|---|
| 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. Comput. Graphique. ? | 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. Comput. Graphique. ? | 2023 | [PUB] [PDF] |
| Federated Learning on Non-IID Graphs via Structural Knowledge Sharing | UTS | AAAI ? | 2023 | [PDF] [CODE] |
| FedGS: Federated Graph-based Sampling with Arbitrary Client Availability | XMU | AAAI ? | 2023 | [PDF] [CODE] |
| An Information Theoretic Perspective for Heterogeneous Subgraph Federated Learning. | PKU | DASFAA | 2023 | [PUB] |
| GraphCS: Graph-based client selection for heterogeneity in federated learning | NUDT | J. Parallel Distributed Comput. | 2023 | [PUB] |
| Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain Approach | BUPT | IEEE Trans. Neural Networks Learn. Syst. | 2023 | [PUB] [PDF] |
| Short-Term Traffic Flow Prediction Based on Graph Convolutional Networks and Federated Learning | ZUEL | IEEE Trans. Intell. Transp. Syst. | 2023 | [PUB] |
| Hyper-Graph Attention Based Federated Learning Methods for Use in Mental Health Detection. | HVL | IEEE J. Biomed. 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. Comput. Soc. Syst. | 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. Mégadonnées | 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 | JEU | Communications naturelles | 2022 | [PUB] [CODE] [解读] |
| Malicious Transaction Identification in Digital Currency via Federated Graph Deep Learning | PEU | 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. | Mégadonnées | 2022 | [PUB] | |
| Domain-Aware Federated Social Bot Detection with Multi-Relational Graph Neural Networks. | UCAS; CAS | IJCNN | 2022 | [PUB] |
| A Federated Multi-Server Knowledge Graph Embedding Framework For Link Prediction. | ICTAI | 2022 | [PUB] | |
| A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy | Ping An Technology | KSEM | 2022 | [PUB] [PDF] |
| Clustered Graph Federated Personalized Learning. | NTNU | IEEECONF | 2022 | [PUB] |
| Investigating the Predictive Reproducibility of Federated Graph Neural Networks using Medical Datasets. | MICCAI Workshop | 2022 | [PDF] [CODE] | |
| Peer-to-Peer Variational Federated Learning Over Arbitrary Graphs | UCSD | Int. J. Bio Inspired |
