Awesome FL
v2024-beta
Inhaltsverzeichnis
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Hinweis zur Repository-Aktualisierung
30.09.2024
Liebe Benutzer, wir möchten Sie über einige Änderungen informieren, die sich auf dieses Open-Source-Repository auswirken. Der Inhaber und Hauptautor @youngfish42 hat sein Doktoratsstudium erfolgreich abgeschlossen? zum 30. September 2024 und hat seitdem seinen Forschungsschwerpunkt verlagert. Diese Änderung der Umstände wird sich auf die Häufigkeit und den Umfang der Aktualisierungen der Papierliste des Repositoriums auswirken.
Anstelle der bisherigen regelmäßigen Aktualisierungen gehen wir davon aus, dass die Papierliste nun monatlich oder vierteljährlich aktualisiert wird. Darüber hinaus wird die Tiefe dieser Updates verringert. Beispielsweise werden Aktualisierungen im Zusammenhang mit der Institution des Autors und dem Open-Source-Code nicht mehr aktiv gepflegt.
Wir verstehen, dass dies Auswirkungen auf den Wert haben könnte, den Sie aus diesem Repository ziehen. Daher laden wir demütig weitere Mitwirkende ein, sich an der Aktualisierung des Inhalts zu beteiligen. Durch diese gemeinsame Anstrengung wird sichergestellt, dass das Repository eine wertvolle Ressource für alle bleibt.
Wir bedanken uns für Ihr Verständnis und freuen uns auf Ihre weitere Unterstützung und Beiträge.
Beste grüße,
白小鱼 (Jungfisch)
Kategorien
Künstliche Intelligenz (IJCAI, AAAI, AISTATS, ALT, AI)
Maschinelles Lernen (NeurIPS, ICML, ICLR, COLT, UAI, Maschinelles Lernen, JMLR, TPAMI)
Data Mining (KDD, WSDM)
Sicher (S&P, CCS, USENIX Security, NDSS)
Computer Vision (ICCV, CVPR, ECCV, MM, IJCV)
Verarbeitung natürlicher Sprache (ACL, EMNLP, NAACL, COLING)
Informationsabruf (SIGIR)
Datenbank (SIGMOD, ICDE, VLDB)
Netzwerk (SIGCOMM, INFOCOM, MOBICOM, NSDI, WWW)
System (OSDI, SOSP, ISCA, MLSys, EuroSys, TPDS, DAC, TOCS, TOS, TCAD, TC)
Andere (ICSE, FOCS, STOC)
| Veranstaltungsort | 2024-2020 | vor 2020 |
|---|---|---|
| IJCAI | 24, 23, 22, 21, 20 | 19 |
| AAAI | 24, 23, 22, 21, 20 | - |
| AISTATS | 24, 23, 22, 21, 20 | - |
| ALT | 22 | - |
| KI (J) | 23 | - |
| NeurIPS | 24, 23, 22, 21, 20 | 18, 17 |
| ICML | 24, 23, 22, 21, 20 | 19 |
| ICLR | 24, 23, 22, 21, 20 | - |
| FOHLEN | 23 | - |
| UAI | 23, 22, 21 | - |
| Maschinelles Lernen (J) | 24, 23, 22 | - |
| JMLR (J) | 24, 23, 22 | - |
| TPAMI (J) | 25, 24, 23, 22 | - |
| KDD | 24, 23, 22, 21, 20 | |
| WSDM | 24, 23, 22, 21 | 19 |
| S&P | 24, 23, 22 | 19 |
| CCS | 24, 23, 22, 21, 19 | 17 |
| USENIX-Sicherheit | 23, 22, 20 | - |
| NDSS | 24, 23, 22, 21 | - |
| CVPR | 24, 23, 22, 21 | - |
| ICCV | 23,21 | - |
| ECCV | 24, 22, 20 | - |
| MM | 24, 23, 22, 21, 20 | - |
| IJCV (J) | 24 | - |
| ACL | 23, 22, 21 | 19 |
| NAACL | 24, 22, 21 | - |
| EMNLP | 24, 23, 22, 21, 20 | - |
| COLING | 20 | - |
| SIGIR | 24, 23, 22, 21, 20 | - |
| SIGMOD | 22, 21 | - |
| ICDE | 24, 23, 22, 21 | - |
| VLDB | 23, 22, 21, 21, 20 | - |
| SIGCOMM | - | - |
| INFOCOM | 24, 23, 22, 21, 20 | 19, 18 |
| MobiCom | 24, 23, 22, 21, 20 | |
| NSDI | 23(1, 2) | - |
| WWW | 24, 23, 22, 21 | |
| OSDI | 21 | - |
| SOSP | 21 | - |
| ISCA | 24 | - |
| MLSys | 24, 23, 22, 20 | 19 |
| EuroSys | 24, 23, 22, 21, 20 | |
| TPDS (J) | 24, 23, 22, 21, 20 | - |
| DAC | 24, 22, 21 | - |
| Inhaltsverzeichnis | - | - |
| AGB | - | - |
| TCAD | 24, 23, 22, 21 | - |
| TC | 24, 23, 22, 21 | - |
| ICSE | 23, 21 | - |
| FOCS | - | - |
| STOC | - | - |
Schlüsselwörter
Statistik: Code ist verfügbar und Sterne >= 100 | Zitat >= 50 | ? Veranstaltungsort der Spitzenklasse
kg. : Wissensgraph | data. : Datensatz | surv. : Umfrage
Artikel zum föderierten Lernen in Nature (und seinen Unterzeitschriften), Cell, Science (und Science Advances) und PANS beziehen sich auf die WOS-Suchmaschine.
| Titel | Zugehörigkeit | Veranstaltungsort | Jahr | Materialien |
|---|---|---|---|---|
| MatSwarm: Vertrauenswürdige, lerngesteuerte Materialberechnung für den Schwarmtransfer für den sicheren Austausch großer Datenmengen | USTB; NTU | Nat. Komm. | 2024 | [PUB] [CODE] |
| Einführung von Edge-Intelligence in intelligente Zähler durch föderiertes Split-Learning | HKU | Nat. Komm. | 2024 | [PUB] [新闻] |
| Eine internationale Studie, die eine föderierte lernende KI-Plattform für pädiatrische Hirntumoren vorstellt | Stanford-Universität | Nat. Komm. | 2024 | [PUB] [CODE] |
| PPML-Omics: Eine datenschutzerhaltende föderierte Methode des maschinellen Lernens schützt die Privatsphäre von Patienten in Omic-Daten | KAUST | Wissenschaftliche Fortschritte | 2024 | [PUB] [CODE] |
| Föderiertes Lernen ist kein Allheilmittel für Datenethik | TUM; UVA | Nat. Mach. Intell.(Kommentar) | 2024 | [PUB] |
| Robustes föderiertes Lernmodell zur Identifizierung von Hochrisikopatienten mit postoperativem Wiederauftreten von Magenkrebs | Zentralkrankenhaus Jiangmen; Guilin Universität für Luft- und Raumfahrttechnik; Guilin Universität für elektronische Technologie; | Nat. Komm. | 2024 | [PUB] [CODE] |
| Selektiver Wissensaustausch für eine datenschutzschonende Verbunddestillation ohne guten Lehrer | HKUST | Nat. Komm. | 2024 | [PUB] [PDF] [CODE] |
| Ein föderiertes Lernsystem für Präzisionsonkologie in Europa: DigiONE | IQVIA Cancer Research BV | Nat. Med. (Kommentar) | 2024 | [PUB] |
| Multi-Client-verteilte blinde Quantenberechnung mit der Qline-Architektur | Sapienza Università di Roma | Nat. Komm. | 2023 | [PUB] [PDF] |
| Geräteunabhängiger Quantenzufall – verbesserter wissensfreier Beweis | USTC | PNAS | 2023 | [PUB] [PDF] [新闻] |
| Kollaborative und datenschutzschonende Sortierung ausgedienter Batterien für profitables direktes Recycling durch föderiertes maschinelles Lernen | Tsinghua-Universität | Nat. Komm. | 2023 | [PUB] |
| Eintreten für den Datenschutz von Neurodaten und die Regulierung der Neurotechnologie | Columbia-Universität | Nat. Protokoll. (Perspektive) | 2023 | [PUB] |
| Verbundbenchmarking medizinischer künstlicher Intelligenz mit MedPerf | IHU Straßburg; Universität Straßburg; Dana-Farber-Krebsinstitut; Weill Cornell Medizin; Harvard TH Chan School of Public Health; MIT; Intel | Nat. Mach. Intel. | 2023 | [PUB] [PDF] [CODE] |
| Algorithmische Gerechtigkeit in der künstlichen Intelligenz für Medizin und Gesundheitswesen | Harvard Medical School; Broad Institute of Harvard und Massachusetts Institute of Technology; Dana-Farber-Krebsinstitut | Nat. Biomed. Ing. (Perspektive) | 2023 | [PUB] [PDF] |
| Differenziell privater Wissenstransfer für föderiertes Lernen | DO | Nat. Komm. | 2023 | [PUB] [CODE] |
| Dezentrales föderiertes Lernen durch gemeinsame Nutzung von Proxy-Modellen | Schicht-6-KI; Universität Waterloo; Vektorinstitut | Nat. Komm. | 2023 | [PUB] [PDF] [CODE] |
| Föderiertes maschinelles Lernen in der datenschutzkonformen Forschung | Universität Hamburg | Nat. Mach. Intell.(Kommentar) | 2023 | [PUB] |
| Föderiertes Lernen zur Vorhersage des histologischen Ansprechens auf eine neoadjuvante Chemotherapie bei dreifach negativem Brustkrebs | Owkin | Nat. Med. | 2023 | [PUB] [CODE] |
| Föderiertes Lernen ermöglicht Big Data für die Erkennung seltener Krebsgrenzen | Universität von Pennsylvania | Nat. Komm. | 2022 | [PUB] [PDF] [CODE] |
| Föderiertes Lernen und Souveränität indigener Genomdaten | Umarmendes Gesicht | Nat. Mach. Intel. (Kommentar) | 2022 | [PUB] |
| Föderiertes entwirrtes Repräsentationslernen zur unbeaufsichtigten Erkennung von Hirnanomalien | TUM | Nat. Mach. Intel. | 2022 | [PUB] [PDF] [CODE] |
| Verlagerung des maschinellen Lernens für das Gesundheitswesen von der Entwicklung zur Bereitstellung und von Modellen zu Daten | Stanford-Universität; Greenstone Biosciences | Nat. Biomed. Ing. (Übersichtsartikel) | 2022 | [PUB] |
| Ein föderiertes graphisches neuronales Netzwerk-Framework für datenschutzfreundliche Personalisierung | DO | Nat. Komm. | 2022 | [PUB] [CODE] [解读] |
| Kommunikationseffizientes föderiertes Lernen durch Wissensdestillation | DO | Nat. Komm. | 2022 | [PUB] [PDF] [CODE] |
| Führendes föderiertes neuromorphes Lernen für drahtlose Edge-künstliche Intelligenz | XMU; NTU | Nat. Komm. | 2022 | [PUB] [CODE] [解读] |
| Ein neuartiger dezentraler, föderierter Lernansatz zum Trainieren auf global verteilten, minderwertigen und geschützten privaten medizinischen Daten | Universität Wollongong | Wissenschaft. Rep. | 2022 | [PUB] |
| Weiterentwicklung der COVID-19-Diagnose durch datenschutzfreundliche Zusammenarbeit im Bereich der künstlichen Intelligenz | HUST | Nat. Mach. Intel. | 2021 | [PUB] [PDF] [CODE] |
| Föderiertes Lernen zur Vorhersage klinischer Ergebnisse bei Patienten mit COVID-19 | MGH-Radiologie und Harvard Medical School | Nat. Med. | 2021 | [PUB] [CODE] |
| Gegnerische Eingriffe und ihre Abhilfemaßnahmen beim datenschutzwahrenden kollaborativen maschinellen Lernen | Imperial College London; TUM; OpenMined | Nat. Mach. Intellekt.(Perspektive) | 2021 | [PUB] |
| Schwarmlernen für dezentrales und vertrauliches klinisches maschinelles Lernen | DZNE; Universität Bonn; | Natur? | 2021 | [PUB] [CODE] [SOFTWARE] [Veröffentlichung] |
| End-to-End-Datenschutz unter Wahrung von Deep Learning zur multiinstitutionellen medizinischen Bildgebung | TUM; Imperial College London; OpenMined | Nat. Mach. Intel. | 2021 | [PUB] [CODE] [解读] |
| Kommunikationseffizientes föderiertes Lernen | CUHK; Princeton-Universität | PFANNEN. | 2021 | [PUB] [CODE] |
| Überwinden Sie die Grenzen des medizinischen Datenaustauschs durch die Verwendung synthetisierter Röntgenbilder | RWTH Aachen | Wissenschaft. Fortschritte. | 2020 | [PUB] [CODE] |
| Sicheres, die Privatsphäre wahrendes und föderiertes maschinelles Lernen in der medizinischen Bildgebung | TUM; Imperial College London; OpenMined | Nat. Mach. Intellekt.(Perspektive) | 2020 | [PUB] |
Federated Learning-Beiträge werden von führenden KI-Konferenzen und -Journalen (Artificial Intelligence) akzeptiert, darunter IJCAI (International Joint Conference on Artificial Intelligence), AAAI (AAAI Conference on Artificial Intelligence), AISTATS (Artificial Intelligence and Statistics) und ALT (International Conference on Algorithmic Learning). Theorie), KI (Künstliche Intelligenz).
| Titel | Zugehörigkeit | Veranstaltungsort | Jahr | Materialien |
|---|---|---|---|---|
| Föderiertes Multi-View-Clustering über Tensorfaktorisierung | IJCAI | 2024 | [PUB] | |
| Effizientes föderiertes Multi-View-Clustering mit integrierter Matrixfaktorisierung und K-Means | IJCAI | 2024 | [PUB] | |
| LG-FGAD: Ein effektives Framework zur Erkennung von Anomalien bei föderierten Graphen | IJCAI | 2024 | [PUB] | |
| Federated Prompt Learning für Weather Foundation-Modelle auf Geräten | IJCAI | 2024 | [PUB] | |
| Barrieren der Systemheterogenität durchbrechen: Nachzügler-tolerantes multimodales föderiertes Lernen durch Wissensdestillation | IJCAI | 2024 | [PUB] | |
| Verlernen während des Lernens: Eine effiziente Methode zum Verlernen von Federated Machine | IJCAI | 2024 | [PUB] | |
| Praktische Hybrid-Gradientenkomprimierung für föderierte Lernsysteme | IJCAI | 2024 | [PUB] | |
| Heterogenitätsbewusste föderierte kausale Entdeckung der Probenqualität durch adaptive Auswahl des Variablenraums | IJCAI | 2024 | [PUB] [CODE] | |
| Feature-Norm Regularisiertes föderiertes Lernen: Nutzung von Datenunterschieden für Modellleistungssteigerungen | IJCAI | 2024 | [PUB] [CODE] | |
| Dirichlet-basierte Unsicherheitsquantifizierung für personalisiertes föderiertes Lernen mit verbesserten posterioren Netzwerken | IJCAI | 2024 | [PUB] | |
| FedConPE: Effiziente föderierte Konversationsbanditen mit heterogenen Kunden | IJCAI | 2024 | [PUB] | |
| DarkFed: Ein datenfreier Backdoor-Angriff im Federated Learning | IJCAI | 2024 | [PUB] | |
| Skalierbares Federated Unlearning über isoliertes und codiertes Sharding | IJCAI | 2024 | [PUB] | |
| Verbesserung der domänenübergreifenden Dual-Target-Empfehlung mit Federated Privacy-Preserving Learning | IJCAI | 2024 | [PUB] | |
| Etikettenverlust beim vertikalen föderierten Lernen: Eine Umfrage | IJCAI | 2024 | [PUB] | |
| Der Aufstieg der Federated Intelligence: Von Federated Foundation-Modellen zur kollektiven Intelligenz | IJCAI | 2024 | [PUB] | |
| LEAP: Hierarchisches föderiertes Lernen zur Optimierung von Nicht-IID-Daten mit Koalitionsbildungsspiel | IJCAI | 2024 | [PUB] | |
| EAB-FL: Verschlimmerung algorithmischer Verzerrungen durch Model-Poisoning-Angriffe beim föderierten Lernen | IJCAI | 2024 | [PUB] | |
| Wissensdestillation im föderierten Lernen: Ein praktischer Leitfaden | IJCAI | 2024 | [PUB] | |
| FedGCS: Ein generatives Framework für eine effiziente Kundenauswahl beim föderierten Lernen mittels Gradienten-basierter Optimierung | IJCAI | 2024 | [PUB] | |
| FedPFT: Federated Proxy-Feinabstimmung von Foundation-Modellen | IJCAI | 2024 | [PUB] [CODE] | |
| Eine systematische Umfrage zum föderierten halbüberwachten Lernen | IJCAI | 2024 | [PUB] | |
| Intelligente Agenten für auktionsbasiertes föderiertes Lernen: Eine Umfrage | IJCAI | 2024 | [PUB] | |
| Eine voreingenommene, umsatzmaximierende Gebotsstrategie für Datenkonsumenten im auktionsbasierten Federated Learning | IJCAI | 2024 | [PUB] | |
| Auf Dualer Kalibrierung basierendes personalisiertes Federated Learning | IJCAI | 2024 | [PUB] | |
| Stakeholderorientierte Entscheidungsunterstützung für auktionsbasiertes föderiertes Lernen | IJCAI | 2024 | [PUB] | |
| Beiträge neu definieren: Shapley-gesteuertes föderiertes Lernen | IJCAI | 2024 | [PUB] [CODE] | |
| Eine Umfrage zu effizienten Federated-Learning-Methoden für das Foundation-Model-Training | IJCAI | 2024 | [PUB] | |
| Von der Optimierung zur Generalisierung: Fair Federated Learning gegen Qualitätsverschiebung durch Inter-Client Sharpness Matching | IJCAI | 2024 | [PUB] [CODE] | |
| FBLG: Ein auf lokalen Graphen basierender Ansatz für den Umgang mit dual verzerrten Nicht-IID-Daten im föderierten Lernen | IJCAI | 2024 | [PUB] | |
| FedFa: Ein vollständig asynchrones Trainingsparadigma für föderiertes Lernen | IJCAI | 2024 | [PUB] | |
| FedSSA: Semantische, auf Ähnlichkeit basierende Aggregation für effizientes modellheterogenes personalisiertes föderiertes Lernen | IJCAI | 2024 | [PUB] | |
| FedES: Gemeinsames Frühstoppen zur Verhinderung des Auswendiglernens heterogener Etikettengeräusche | IJCAI | 2024 | [PUB] | |
| Personalisiertes Verbundlernen für die stadtübergreifende Verkehrsvorhersage | IJCAI | 2024 | [PUB] | |
| Federated Adaptation für Foundation Model-basierte Empfehlungen | IJCAI | 2024 | [PUB] | |
| BADFSS: Backdoor-Angriffe auf föderiertes selbstüberwachtes Lernen | IJCAI | 2024 | [PUB] | |
| Schätzen vor Debiasing: Ein Bayesianischer Ansatz zur Beseitigung früherer Bias beim Federated Semi-Supervised Learning | IJCAI | 2024 | [PUB] [CODE] | |
| FedTAD: Topologiebewusste, datenfreie Wissensdestillation für Subgraph Federated Learning | IJCAI | 2024 | [PUB] | |
| BOBA: Byzantinisch-robustes föderiertes Lernen mit Etikettenschiefe | UIUC | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| Föderierte lineare kontextuelle Banditen mit heterogenen Clients | Universität von Virginia | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| Federated Experiment Design unter Distributed Differential Privacy | Stanford-Universität; Meta | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| Umgehen von Sattelpunkten beim heterogenen föderierten Lernen durch verteiltes SGD mit Kommunikationskomprimierung | Princeton-Universität | AISTATS | 2024 | [PUB] [PDF] |
| Asynchrones SGD in Diagrammen: ein einheitliches Framework für asynchrone dezentrale und föderierte Optimierung | INRIA | AISTATS | 2024 | [PUB] [PDF] |
| SIFU: Sequential Informed Federated Unlearning für effizientes und nachweisbares Client-Unlearning bei der Federated Optimization | INRIA | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| Komprimierung mit exakter Fehlerverteilung für Federated Learning | École Polytechnique | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| Adaptive Federated Minimax-Optimierung mit geringerer Komplexität | NJU; MIIT-Schlüssellabor für Musteranalyse und maschinelle Intelligenz | AISTATS | 2024 | [PUB] [PDF] |
| Adaptive Komprimierung beim föderierten Lernen über Nebeninformationen | Stanford-Universität; Universität Padua | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| On-Demand-Verbundlernen für beliebige Zielklassenverteilungen | UNIST | AISTATS | 2024 | [PUB] [CODE] |
| FedFisher: Nutzung von Fisher-Informationen für One-Shot Federated Learning | CMU | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| Warteschlangendynamik des asynchronen Federated Learning | Huawei | AISTATS | 2024 | [PUB] [PDF] |
| Personalisierter Föderierter X-armiger Bandit | Purdue-Universität | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| Föderiertes Lernen für heterogene elektronische Gesundheitsakten unter Verwendung von Augmented Temporal Graph Attention Networks | Universität Oxford | AISTATS | 2024 | [PUB] [CODE] |
| Stochastischer, geglätteter Gradientenabstiegsanstieg für die föderierte Minimax-Optimierung | Universität von Virginia | AISTATS | 2024 | [PUB] [PDF] |
| Verallgemeinerung des föderierten Lernens durch Stabilität verstehen: Heterogenität ist wichtig | Nordwestliche Universität | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| Nachweisbare gegenseitige Vorteile von Federated Learning in datenschutzsensiblen Bereichen | Universität Sofia | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| Analyse von Datenschutzlecks in föderierten großen Sprachmodellen | Universität von Florida | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| Invarianter Aggregator zur Abwehr von Federated Backdoor-Angriffen | UIUC | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| Kommunikationseffizientes föderiertes Lernen mit Daten- und Kundenheterogenität | ISTA | AISTATS | 2024 | [PUB] [PDF] [CODE] |
| FedMut: Generalisiertes föderiertes Lernen durch stochastische Mutation | NTU | AAAI | 2024 | [PUB] |
| Föderiertes partielles Label-Lernen mit lokal-adaptiver Augmentation und Regularisierung | Carleton-Universität | AAAI | 2024 | [PUB] [SEITE] |
| Keine Vorurteile! Fair Federated Graph Neural Networks für personalisierte Empfehlungen | ICH S | AAAI | 2024 | [PUB] [SEITE] [PDF] [CODE] |
| Formale Logik ermöglichte personalisiertes föderiertes Lernen durch Eigenschaftsinferenz | Vanderbilt-Universität | AAAI | 2024 | [PUB] [PDF] |
| Aufgabenunabhängiges, datenschutzerhaltendes Repräsentationslernen für föderiertes Lernen gegen Attributinferenzangriffe | Illinois Tech | AAAI | 2024 | [PUB] [SEITE] [PDF] [CODE] |
| FairTrade: Pareto-optimale Kompromisse zwischen ausgewogener Genauigkeit und Fairness beim föderierten Lernen erreichen | Leibniz Universität | AAAI | 2024 | [PUB] [SEITE] |
| Bekämpfung von Datenungleichgewichten beim föderierten halbüberwachten Lernen mit dualen Regulierungsbehörden | HKUST | AAAI | 2024 | [PUB] [SEITE] [PDF] |
| Fed-QSSL: Ein Framework für personalisiertes Federated Learning unter Bitbreite und Datenheterogenität | UT | AAAI | 2024 | [PUB] [SEITE] [PDF] |
| Zur Entflechtung des asymmetrischen Wissenstransfers für modalitätsaufgabenunabhängiges föderiertes Lernen | Universität von Virginia | AAAI | 2024 | [PUB] |
| FedDAT: Ein Ansatz zur Feinabstimmung des Basismodells beim multimodalen heterogenen föderierten Lernen | LMU München Siemens AG | AAAI | 2024 | [PUB] [SEITE] [PDF] [CODE] |
| Passen Sie auf Ihren Kopf auf: Zusammenbau von Projektionsköpfen, um die Zuverlässigkeit föderierter Modelle zu schützen | Gemeinsames Schlüssellabor für künstliche Intelligenz der Xi'an Jiaotong University Shaanxi | AAAI | 2024 | [PUB] [SEITE] [PDF] |
| FedGCR: Erzielung von Leistung und Fairness für Federated Learning mit unterschiedlichen Kundentypen durch Gruppenanpassung und Neugewichtung | NTU | AAAI | 2024 | [PUB] [SEITE] [CODE] |
| Föderierte modalitätsspezifische Encoder und multimodale Anker für die personalisierte Segmentierung von Hirntumoren | Xiamen-Universität | AAAI | 2024 | [PUB] [SEITE] [PDF] [CODE] |
| Ausnutzen von Label-Skews beim Federated Learning mit Modellverkettung | NUS | AAAI | 2024 | [PUB] [SEITE] [PDF] [CODE] |
| Komplementäre Wissensdestillation für ein robustes und die Privatsphäre schützendes Modell für vertikales föderiertes Lernen | SUST; HKUST | AAAI | 2024 | [PUB] [SEITE] |
| Föderiertes Lernen durch kollaborative Input-Output-Destillation | Universität in Buffalo; USA Harvard Medical School | AAAI | 2024 | [PUB] [SEITE] [PDF] [CODE] |
| Kalibriertes föderiertes Ein-Runden-Lernen mit Bayes'scher Inferenz im prädiktiven Raum | Vektorinstitut der University of Waterloo | AAAI | 2024 | [PUB] [SEITE] [PDF] [CODE] |
| FedCSL: Ein skalierbarer und genauer Ansatz für das Lernen föderierter Kausalstrukturen | HFUT | AAAI | 2024 | [PUB] [PDF] |
| FedFixer: Minderung heterogener Label-Rauschen beim föderierten Lernen | Xi'an Jiaotong Universität; Universität Leiden | AAAI | 2024 | [PUB] [SEITE] [PDF] |
| FedLPS: Heterogenes Federated Learning für mehrere Aufgaben mit lokaler Parameterfreigabe | NJU | AAAI | 2024 | [PUB] [SEITE] [PDF] [CODE] |
| Nachweislich konvergentes föderiertes Trilevel-Lernen | TJU | AAAI | 2024 | [PUB] [PDF] |
| Performatives Federated Learning: Eine Lösung für modellabhängige und heterogene Verteilungsverschiebungen | ÄH | AAAI | 2024 | [PUB] [SEITE] |
| General Commerce Intelligence: Glocally Federated NLP-basierte Engine für datenschutzschonende und nachhaltige personalisierte Dienste von Multi-Händlern | Kyung-Hee-Universität; Harex InfoTech | AAAI | 2024 | [PUB] [SEITE] |
| EMGAN: Early-Mix-GAN zum Extrahieren serverseitiger Modelle in Split Federated Learning | Sony KI | AAAI | 2024 | [PUB] [SEITE] [CODE] |
| FedDiv: Kollaborative Rauschfilterung für föderiertes Lernen mit verrauschten Etiketten | SYSU; HKU | AAAI | 2024 | [PUB] [SEITE] [PDF] [CODE] |
| Punkttransformator mit föderiertem Lernen zur Vorhersage des HER2-Status von Brustkrebs anhand von mit Hämatoxylin und Eosin gefärbten Bildern ganzer Objektträger | USTC; CAS | AAAI | 2024 | [PUB] [SEITE] [PDF] [CODE] |
| FedNS: Ein schnell skizzierender Newton-Algorithmus für föderiertes Lernen | CAS | AAAI | 2024 | [PUB] [PDF] [CODE] |
| Föderierter X-bewaffneter Bandit | Purdue-Universität | AAAI | 2024 | [PUB] [SEITE] [PDF] [CODE] |
| Algorithmische Grundlage des föderierten Lernens mit sequentiellen Daten | GMU | AAAI | 2024 | [PUB] |
| UFDA: Universelle föderierte Domänenanpassung mit praktischen Annahmen | XJTU; Universität Sydney | AAAI | 2024 | [PUB] [SEITE] [PDF] [CODE] |
| FedASMU: Effizientes asynchrones föderiertes Lernen mit dynamischer, veralteter Modellaktualisierung | Hithink RoyalFlush Information Network Co | AAAI | 2024 | [PUB] [SEITE] [PDF] |
| Sprachgesteuerter Transformator für die föderierte Multi-Label-Klassifizierung | NTU | AAAI | 2024 | [PUB] [SEITE] [PDF] [CODE] |
| FedCD: Föderiertes halbüberwachtes Lernen mit ausgewogenem Klassenbewusstsein durch zwei Lehrer | SZU | AAAI | 2024 | [PUB] [SEITE] [CODE] |
| Jenseits traditioneller Bedrohungen: Ein anhaltender Backdoor-Angriff auf Federated Learning | HEU | AAAI | 2024 | [PUB] [SEITE] [CODE] |
| Föderiertes Lernen mit extrem lauten Clients durch negative Destillation | XMU | AAAI | 2024 | [PUB] [SEITE] [PDF] [CODE] |
| FedST: Federated Style Transfer Learning für Nicht-IID-Bildsegmentierung | USTB | AAAI | 2024 | [PUB] [SEITE] [学报] [CODE] |
| PPIDSG: Ein datenschutzschonendes Bildverteilungs-Sharing-Schema mit GAN in Federated Learning | USTC | AAAI | 2024 | [PUB] [SEITE] [PDF] [CODE] |
| Ein datenschutzerhaltendes Federated Learning (PPFL)-basiertes Cognitive Digital Twin (CDT)-Framework für Smart Cities | DCU | AAAI | 2024 | [PUB] |
| Ein Primal-Dual-Algorithmus für hybrides föderiertes Lernen | Nordwestliche Universität | AAAI | 2024 | [PUB] [SEITE] [PDF] |
| FedLF: Layer-Wise Fair Federated Learning | CUHK; Shenzhen Institut für Künstliche Intelligenz und Robotik für die Gesellschaft | AAAI | 2024 | [PUB] [SEITE] |
| Auf dem Weg zum Fair Graph Federated Learning über Anreizmechanismen | ZJU; FDU | AAAI | 2024 | [PUB] [SEITE] [PDF] [CODE] |
| Auf dem Weg zur Robustheit des differenziell privaten föderierten Lernens | DO | AAAI | 2024 | [PUB] [SEITE] |
| Abwehr von Hintertürangriffen beim föderierten Lernen durch bidirektionale Wahlen und individuelle Perspektive | ZJU; Huawei | AAAI | 2024 | [PUB] [SEITE] [PDF] [CODE] |
| Ganzzahl ist genug: Wenn vertikales föderiertes Lernen auf Rundung trifft | ZJU; Ameisengruppe | AAAI | 2024 | [PUB] [SEITE] |
| CLIP-gesteuertes föderiertes Lernen zu Heterogenität und Long-Tailed-Daten | XMU | AAAI | 2024 | [PUB] [SEITE] [PDF] [CODE] |
| Federated Adaptive Prompt Tuning für kollaboratives Lernen in mehreren Domänen | FDU | AAAI | 2024 | [PUB] [SEITE] [PDF] [CODE] |
| Mehrdimensionales faires föderiertes Lernen | SDU | AAAI | 2024 | [PUB] [SEITE] [PDF] |
| HiFi-Gas: Hierarchischer föderierter Lernanreizmechanismus, verbesserte Schätzung des Gasverbrauchs | ENN-Gruppe | AAAI | 2024 | [PUB] |
| Zur Rolle der Serverdynamik beim Federated Learning | Universität von Virginia | AAAI | 2024 | [PUB] [PDF] |
| FedCompetitors: Harmonische Zusammenarbeit beim Federated Learning mit konkurrierenden Teilnehmern | BUPT | AAAI | 2024 | [PUB] [SEITE] [PDF] |
| z-SignFedAvg: Eine einheitliche stochastische zeichenbasierte Komprimierung für föderiertes Lernen | CUHK; China Shenzhen Forschungsinstitut für Big Data | AAAI | 2024 | [PUB] [SEITE] [PDF] |
| Datendisparität und zeitliche Nichtverfügbarkeit bewusstes asynchrones föderiertes Lernen für die vorausschauende Wartung von Transportflotten | Volkswagen-Konzern | AAAI | 2024 | [PUB] [SEITE] |
| Federated Graph Learning unter Domain Shift mit generalisierbaren Prototypen | WHU | AAAI | 2024 | [PUB] [SEITE] |
| TurboSVM-FL: Förderung des föderierten Lernens durch SVM-Aggregation für Lazy Clients | Technische Universität München | AAAI | 2024 | [PUB] [SEITE] [PDF] [CODE] |
| Kollaborative Gradientendiskrepanzminimierung aus mehreren Quellen für die Generalisierung föderierter Domänen | TJU | AAAI | 2024 | [PUB] [PDF] [CODE] |
| Verbergen sensibler Proben vor Gradientenlecks beim föderierten Lernen | Monash-Universität | AAAI | 2024 | [PUB] [SEITE] [PDF] [CODE] |
| FedA3I: Annotation Quality-Aware Aggregation für die föderierte medizinische Bildsegmentierung gegen heterogenes Annotation Noise | HUST | AAAI | 2024 | [PUB] [SEITE] [PDF] [CODE] |
| Föderiertes Kausalitätslernen mit erklärbarer adaptiver Optimierung | SDU | AAAI | 2024 | [PUB] [SEITE] [PDF] |
| Föderierte kontextuelle kaskadierende Banditen mit asynchroner Kommunikation und heterogenen Benutzern | USTC | AAAI | 2024 | [PUB] [SEITE] [PDF] |
| Erkundung des halbüberwachten Verbundlernens mit einem Schuss mit vorab trainierten Diffusionsmodellen | FDU | AAAI | 2024 | [PUB] [PDF] |
| Durch Diversität und Authentizität eingeschränkte Stilisierung für die Generalisierung föderierter Domänen bei der erneuten Identifizierung von Personen | XMU; Universität Trient | AAAI | 2024 | [PUB] [SEITE] |
| PerFedRLNAS: Eine für alle personalisierte föderierte neuronale Architektursuche | U von T | AAAI | 2024 | [PUB] [SEITE] |
| Effizientes asynchrones föderiertes Lernen mit prospektiver Impulsaggregation und feinkörniger Korrektur | BUPT | AAAI | 2024 | [PUB] [SEITE] |
| Gegnerische Angriffe auf Federated-Learned Adaptive Bitrate-Algorithmen | HKU | AAAI | 2024 | [PUB] |
| FedTGP: Trainierbare globale Prototypen mit Adaptive-Margin-Enhanced Contrastive Learning für Daten- und Modellheterogenität im Federated Learning | SJTU | AAAI | 2024 | [PUB] [SEITE] [PDF] [CODE] |
| LR-XFL: Auf logischem Denken basierendes, erklärbares föderiertes Lernen | NTU | AAAI | 2024 | [PUB] [PDF] [CODE] |
| Ein Huber-Verlustminimierungsansatz für byzantinisches robustes föderiertes Lernen | Zhejiang-Labor | AAAI | 2024 | [PUB] [SEITE] [PDF] |
| Wissensbewusstes Parameter-Coaching für personalisiertes föderiertes Lernen | Nordöstliche Universität | AAAI | 2024 | [PUB] [SEITE] |
| Föderiertes Label-Noise-Lernen mit lokaler Diversitätsprodukt-Regularisierung | SJTU | AAAI | 2024 | [PUB] [PAGE] [SUPP] |
| Angepasste gewichtete Aggregation im föderierten Lernen (Studentenzusammenfassung) | UBC | AAAI | 2024 | [PUB] |
| Wissenstransfer über ein kompaktes Modell im Federated Learning (Student Abstract) | Universität Sydney | AAAI | 2024 | [PUB] [SEITE] |
| PICSR: Prototypbasierter Cross-Silo-Router für föderiertes Lernen (Studentenzusammenfassung) | Das Auton Lab der Ohio State University, CMU | AAAI | 2024 | [PUB] [SEITE] |
| Datenschutzerhaltendes Graph-Faltungsnetzwerk für die Empfehlung föderierter Elemente | SZU | KI | 2023 | [PUB] |
| Win-win-Situation: Ein datenschutzerhaltendes föderiertes Framework für domänenübergreifende Dual-Target-Empfehlungen | CAS; UCAS; JD-Technologie; JD Intelligent Cities Research | AAAI | 2023 | [PUB] |
| Ungezielter Angriff auf föderierte Empfehlungssysteme über die Einbettung giftiger Gegenstände und die Verteidigung | USTC; Staatliches Schlüssellabor für kognitive Intelligenz | AAAI | 2023 | [PUB] [PDF] [CODE] |
| Anreizgestütztes föderiertes Crowdsourcing | SDU | AAAI | 2023 | [PUB] [PDF] |
| Bewältigung der Datenheterogenität beim föderierten Lernen mit Klassenprototypen | Lehigh-Universität | AAAI | 2023 | [PUB] [PDF] [CODE] |
| FairFed: Ermöglichung von Gruppengerechtigkeit beim föderierten Lernen | USC | AAAI | 2023 | [PUB] [PDF] [PDF] |
| Verbreitung der föderierten Robustheit: Gemeinsame kontroverse Robustheit beim heterogenen föderierten Lernen | MSU | AAAI | 2023 | [PUB] |
| Komplementsparsifizierung: Modellbereinigung mit geringem Overhead für föderiertes Lernen | NJIT | AAAI | 2023 | [PUB] |
| Nahezu kostenlose Kommunikation bei der Federated Best Arm Identification | NUS | AAAI | 2023 | [PUB] [PDF] |
| Schichtweise adaptive Modellaggregation für skalierbares Federated Learning | University of Southern California Inha University | AAAI | 2023 | [PUB] [PDF] |
| Vergiftung mit Cerberus: Heimlicher und heimlicher Hintertürangriff gegen Federated Learning | BJTU | AAAI | 2023 | [PUB] |
| FedMDFG: Federated Learning mit Multi-Gradient-Abstieg und fairer Anleitung | CUHK; Das Shenzhen Institute of Artificial Intelligence and Robotics for Society | AAAI | 2023 | [PUB] |
| Sichere Aggregation sichern: Mehrseitige Datenschutzlecks beim Federated Learning abmildern | USC | AAAI | 2023 | [PUB] [PDF] [VIDEO] [CODE] |
| Föderiertes Lernen auf Nicht-IID-Graphen durch strukturellen Wissensaustausch | UTS | AAAI | 2023 | [PUB] [PDF] [CODE] |
| Effiziente Verteilungsähnlichkeitsidentifizierung beim Clustered Federated Learning über Hauptwinkel zwischen Client-Daten-Unterräumen | UCSD | AAAI | 2023 | [PUB] [PDF] [CODE] |
| FedABC: Fairen Wettbewerb beim personalisierten Verbundlernen anstreben | WHU; Hubei Luojia-Labor; JD Explore Academy | AAAI | 2023 | [PUB] [PDF] |
| Jenseits von ADMM: Ein einheitliches, kundenvarianzreduziertes, adaptives Federated-Learning-Framework | SUTD | AAAI | 2023 | [PUB] [PDF] |
| FedGS: Federated Graph-Based Sampling mit beliebiger Client-Verfügbarkeit | XMU | AAAI | 2023 | [PUB] [PDF] [CODE] |
| Schnelleres adaptives föderiertes Lernen | Universität Pittsburgh | AAAI | 2023 | [PUB] [PDF] |
| FedNP: Auf dem Weg zum nicht-IID-verbundenen Lernen durch föderierte neuronale Ausbreitung | HKUST | AAAI | 2023 | [PUB] [CODE] [VIDEO] [SUPP] |
| Bayesian Federated Neural Matching, das vollständige Informationen vervollständigt | TJU | AAAI | 2023 | [PUB] [PDF] |
| CDMA: Ein praktischer geräteübergreifender Federated-Learning-Algorithmus für allgemeine Minimax-Probleme | ZJU | AAAI | 2023 | [PUB] [PDF] [CODE] |
| Föderiertes generatives Modell für heterogene Daten aus mehreren Quellen im IoT | GSU | AAAI | 2023 | [PUB] |
| DeFL: Abwehr von Model-Poisoning-Angriffen im Federated Learning durch Sensibilisierung für kritische Lernperioden | SUNY-Binghamton University | AAAI | 2023 | [PUB] |
| FedALA: Adaptive lokale Aggregation für personalisiertes föderiertes Lernen | SJTU | AAAI | 2023 | [PUB] [PDF] [CODE] |
| Eintauchen in die kontroverse Robustheit des föderierten Lernens | ZJU | AAAI | 2023 | [PUB] [PDF] |
| Zur Anfälligkeit von Backdoor-Abwehrmaßnahmen für Federated Learning | TJU | AAAI | 2023 | [PUB] [PDF] [CODE] |
| Echo of Neighbors: Datenschutzverstärkung für personalisiertes privates Verbundlernen mit Shuffle-Modell | RUC; Technisches Forschungszentrum des Bildungsministeriums für Datenbanken und BI | AAAI | 2023 | [PUB] [PDF] |
| DPAUC: Differenziell private AUC-Berechnung im föderierten Lernen | ByteDance Inc. | AAAI-Spezialstrecken | 2023 | [PUB] [PDF] [CODE] |
| Effizientes Training groß angelegter industrieller Fehlerdiagnosemodelle durch Federated Opportunistic Block Dropout | NTU | AAAI-Sonderprogramme | 2023 | [PUB] [PDF] |
| Branchenweit orchestriertes gemeinsames Lernen für die Arzneimittelforschung | KU Leuven | AAAI-Sonderprogramme | 2023 | [PUB] [PDF] [VIDEO] |
| Ein föderiertes Lernüberwachungstool für die Simulation selbstfahrender Autos (Studentenzusammenfassung) | CNU | AAAI-Sonderprogramme | 2023 | [PUB] |
| MGIA: Gegenseitiger Gradienteninversionsangriff beim multimodalen föderierten Lernen (Studentenzusammenfassung) | PolyU | AAAI-Sonderprogramme | 2023 | [PUB] |
| Clustered Federated Learning für heterogene Daten (Studentenzusammenfassung) | RUC | AAAI-Sonderprogramme | 2023 | [PUB] |
| FedSampling: Eine bessere Sampling-Strategie für Federated Learning | DO | IJCAI | 2023 | [PUB] [PDF] [CODE] |
| HyperFed: Erforschung hyperbolischer Prototypen mit konsistenter Aggregation für Nicht-IID-Daten im Federated Learning | ZJU | IJCAI | 2023 | [PUB] [PDF] |
| FedOBD: Opportunistischer Block-Dropout für das effiziente Training großer neuronaler Netze durch Federated Learning | NTU | IJCAI | 2023 | [PUB] [PDF] [CODE] |
| Föderierte probabilistische Präferenzverteilungsmodellierung mit kompaktem Co-Clustering für datenschutzerhaltende Empfehlungen für mehrere Domänen | ZJU | IJCAI | 2023 | [PUB] |
| Semantisches und strukturelles Lernen von föderierten Graphen | WHU | IJCAI | 2023 | [PUB] |
| BARA: Effizienter Anreizmechanismus mit Online-Zuweisung von Belohnungsbudgets beim Cross-Silo Federated Learning | SYSU | IJCAI | 2023 | [PUB] [PDF] |
| FedDWA: Personalisiertes Federated Learning mit dynamischer Gewichtsanpassung | SYSU | IJCAI | 2023 | [PUB] [PDF] |
| FedPass: Datenschutzerhaltendes Vertical Federated Deep Learning mit adaptiver Verschleierung | Webank | IJCAI | 2023 | [PUB] [PDF] |
| Global konsistenter Federated Graph Autoencoder für Nicht-IID-Diagramme | FZU | IJCAI | 2023 | [PUB] [CODE] |
| Wettbewerbs-kooperatives Multi-Agent-Reinforcement-Lernen für auktionsbasiertes föderiertes Lernen | NTU | IJCAI | 2023 | [PUB] |
| Doppelte Personalisierung auf Verbundempfehlung | JLU; Technische Universität Sydney | IJCAI | 2023 | [PUB] [PDF] [CODE] |
| FedNoRo: Auf dem Weg zu lärmrobustem föderiertem Lernen durch Beseitigung von Klassenungleichgewichten und Label-Rauschen-Heterogenität | HUST | IJCAI | 2023 | [PUB] [PDF] [CODE] |
| Denial-of-Service oder feinkörnige Kontrolle: Auf dem Weg zu flexiblen Model-Poisoning-Angriffen auf föderiertes Lernen | Xiangtan-Universität | IJCAI | 2023 | [PUB] [PDF] [CODE] |
| FedHGN: Ein föderiertes Framework für heterogene graphische neuronale Netze | CUHK | IJCAI | 2023 | [PUB] [PDF] [CODE] |
| FedET: Ein kommunikationseffizientes föderiertes klasseninkrementelles Lernframework basierend auf Enhanced Transformer | Ping-An-Technologie; DO | IJCAI | 2023 | [PUB] [PDF] |
| Schnelles föderiertes Lernen für die Wettervorhersage: Auf dem Weg zu Grundlagenmodellen für meteorologische Daten | UTS | IJCAI | 2023 | [PUB] [PDF] [CODE] |
| FedBFPT: Ein effizientes Federated Learning Framework für die weitere Vorschulung von Bert | ZJU | IJCAI | 2023 | [PUB] [CODE] |
| Bayesian Federated Learning: Eine Umfrage | IJCAI-Umfrage-Track | 2023 | [PDF] | |
| Eine Übersicht über die föderierte Evaluation im föderierten Lernen | Macquarie-Universität | IJCAI-Umfrage-Track | 2023 | [PUB] [PDF] |
| SAMBA: Ein generisches Framework für sichere föderierte mehrarmige Banditen (erweiterte Zusammenfassung) | INSA-Zentrum Val de Loire | IJCAI Journal Track | 2023 | [PUB] |
| Die Kommunikationskosten für Sicherheit und Datenschutz bei der föderierten Frequenzschätzung | Stanford | AISTATS | 2023 | [PUB] [CODE] |
| Effizientes und leichtes föderiertes Lernen über asynchrones verteiltes Dropout | Reisuniversität | AISTATS | 2023 | [PUB] [CODE] |
| Föderiertes Lernen unter verteilter Konzeptdrift | CMU | AISTATS | 2023 | [PUB] [CODE] |
| Charakterisierung interner Umgehungsangriffe beim Federated Learning | CMU | AISTATS | 2023 | [PUB] [CODE] |
| Federated Asymptotics: ein Modell zum Vergleich von föderierten Lernalgorithmen | Stanford | AISTATS | 2023 | [PUB] [CODE] |
| Privates, nicht-konvexes föderiertes Lernen ohne vertrauenswürdigen Server | USC | AISTATS | 2023 | [PUB] [CODE] |
| Föderiertes Lernen für Datenströme | Universität an der Côte d'Azur | AISTATS | 2023 | [PUB] [CODE] |
| Nichts als Bedauern – Datenschutz-wahrende Federated Causal Discovery | Helmholtz-Zentrum für Informationssicherheit | AISTATS | 2023 | [PUB] [CODE] |
| Aktiver Mitgliedschaftsinferenzangriff unter lokaler differenzieller Privatsphäre im föderierten Lernen | UFL | AISTATS | 2023 | [PUB] [CODE] |
| Federated Averaging Langevin Dynamics: Auf dem Weg zu einer einheitlichen Theorie und neuen Algorithmen | CMAP | AISTATS | 2023 | [PUB] |
| Byzantinisch-robustes föderiertes Lernen mit optimalen statistischen Raten | UC Berkeley | AISTATS | 2023 | [PUB] [CODE] |
| Föderiertes Lernen auf Nicht-IID-Graphen durch strukturellen Wissensaustausch | UTS | AAAI | 2023 | [PDF] [CODE] |
| FedGS: Federated Graph-based Sampling mit beliebiger Client-Verfügbarkeit | XMU | AAAI | 2023 | [PDF] [CODE] |
| Anreizgestütztes Federated Crowdsourcing | SDU | AAAI | 2023 | [PDF] |
| Auf dem Weg zum Verständnis der voreingenommenen Kundenauswahl beim föderierten Lernen. | CMU | AISTATS | 2022 | [PUB] [CODE] |
| FLIX: Eine einfache und kommunikationseffiziente Alternative zu lokalen Methoden im Federated Learning | KAUST | AISTATS | 2022 | [PUB] [PDF] [CODE] |
| Scharfe Grenzen für Federated Averaging (Local SGD) und kontinuierliche Perspektive. | Stanford | AISTATS | 2022 | [PUB] [PDF] [CODE] |
| Federated Reinforcement Learning mit Umgebungsheterogenität. | PKU | AISTATS | 2022 | [PUB] [PDF] [CODE] |
| Föderierte kurzsichtige Community-Erkennung mit One-Shot-Kommunikation | Purdue | AISTATS | 2022 | [PUB] [PDF] |
| Asynchrone Algorithmen mit oberer Vertrauensgrenze für Federated Linear Bandits. | Universität von Virginia | AISTATS | 2022 | [PUB] [PDF] [CODE] |
| In Richtung der föderierten Bayesian -Netzwerkstruktur mit kontinuierlicher Optimierung. | CMU | Aistats | 2022 | [Pub] [PDF] [Code] |
| Föderiertes Lernen mit gepufferter asynchroner Aggregation | Meta Ai | Aistats | 2022 | [Pub] [PDF] [Video] |
| Differenziell privates Federated -Lernen für heterogene Daten. | Stanford | Aistats | 2022 | [Pub] [PDF] [Code] |
| Sparsefed: mildernde Modellvergiftungsangriffe im Föderierten Lernen mit Sparsifikation | Princeton | Aistats | 2022 | [Pub] [PDF] [Code] [Video] |
| Basisangelegenheiten: Bessere, kommunikationseffiziente Methoden zweiter Ordnung für das Föderierte Lernen | KAUST | Aistats | 2022 | [Pub] [PDF] |
| Föderierte Funktionsgradientenverstärkung. | Universität von Pennsylvania | Aistats | 2022 | [Pub] [PDF] [Code] |
| QLSD: Quantisierte Langevin Stochastische Dynamik für das Lernen von Bayesian Federated. | Criteo AI Lab | Aistats | 2022 | [Pub] [PDF] [Code] [Video] |
Meta-Learning-basierte Wissens Extrapolation für Wissensgrafiken im Verbundkg kg. | Zju | Ijcai | 2022 | [Pub] [PDF] [Code] |
| Personalisiertes föderiertes Lernen mit einer Grafik | UTS | Ijcai | 2022 | [Pub] [PDF] [Code] |
| Vertikalvertisch föderiertes Graph Neuronales Netz | Zju | Ijcai | 2022 | [Pub] [PDF] |
| Anpassen an die Anpassung: Lernen der Personalisierung für das Cross-Silo-Verbund lernen | Ijcai | 2022 | [Pub] [PDF] [Code] | |
| Heterogener Ensemble -Wissenstransfer für die Schulung großer Modelle im Verbundlernen | Ijcai | 2022 | [Pub] [PDF] | |
| Privates, semi-überprüfter Föderierten. | Ijcai | 2022 | [Pub] | |
| Kontinuierliches Föderierte Lernen basierend auf Wissensdestillation. | Ijcai | 2022 | [Pub] | |
| Föderiertes Lernen auf heterogenen und langschwanzigen Daten über Klassifizierer, die wieder mit Föderierten Merkmalen ausgestattet sind | Ijcai | 2022 | [Pub] [PDF] [Code] | |
| Föderierte Aufmerksamkeit der Multitasking-Aufmerksamkeit für die kräftig-individuelle Erkennung menschlicher Aktivitäten | Ijcai | 2022 | [Pub] | |
| Personalisiertes föderiertes Lernen mit kontextualisierter Verallgemeinerung. | Ijcai | 2022 | [Pub] [PDF] | |
| Abschirmungsböder -Lernen: Robuste Aggregation mit adaptiver Kundenauswahl. | Ijcai | 2022 | [Pub] [PDF] | |
| FEDCG: Nutzen Sie bedingte GAN, um die Privatsphäre zu schützen und die Wettbewerbsleistung beim Föderierten Lernen aufrechtzuerhalten | Ijcai | 2022 | [Pub] [PDF] [Code] | |
| FEDDUAP: Federated Learning mit dynamischem Update und adaptiver Beschneidung mithilfe gemeinsamer Daten auf dem Server. | Ijcai | 2022 | [Pub] [PDF] | |
Auf nachprüfbaren Föderierten Lernen surv. | Ijcai | 2022 | [Pub] [PDF] | |
| Harmofl: Harmonisierung lokaler und globaler Drifts im Verbundlernen auf heterogenen medizinischen Bildern | Cuhk; Buaa | Aaai | 2022 | [Pub] [PDF] [Code] [解读] |
| Föderiertes Lernen für die Gesichtserkennung mit Gradientenkorrektur | Bupt | Aaai | 2022 | [Pub] [PDF] |
| SpreadGnn: Dezentrales Multi-Task-Föderierte Lernen für Grafiknetzwerke für molekulare Daten | USC | Aaai | 2022 | [Pub] [PDF] [Code] [解读] |
| SmartIDX: Reduzierung der Kommunikationskosten beim Föderierten Lernen durch Nutzung der CNNS -Strukturen | SCHLAG; PCL | Aaai | 2022 | [Pub] [Code] |
| Überbrückung zwischen kognitiven Verarbeitungssignalen und sprachlichen Merkmalen über ein einheitliches Aufmerksamkeitsnetzwerk | Tju | Aaai | 2022 | [Pub] [PDF] |
| Kritische Lernperioden im Föderierten Lernen ergreifen | Suny-Binghamton University | Aaai | 2022 | [Pub] [PDF] |
| Koordinierung der Impulse für das Lernen mit Cross-Silo-Verbänden | Universität von Pittsburgh | Aaai | 2022 | [Pub] [PDF] |
| FedProto: Föderierte Prototyp -Lernen über heterogene Geräte | UTS | Aaai | 2022 | [Pub] [PDF] [Code] |
| Fedsoft: Soft Clustered Federated Learning mit proximaler lokaler Aktualisierung | CMU | Aaai | 2022 | [Pub] [PDF] [Code] |
| Föderierte dynamische spärliche Schulung: weniger berechnen, weniger kommunizieren, aber besser lernen | Die Universität von Texas in Austin | Aaai | 2022 | [Pub] [PDF] [Code] |
| FEDFR: Joint Optimization Federated Framework für generische und personalisierte Gesichtserkennung | National Taiwan University | Aaai | 2022 | [Pub] [PDF] [Code] |
| Splitfed: Wenn Federated Learning geteiltes Lernen trifft | Csiro | Aaai | 2022 | [Pub] [PDF] [Code] |
| Effiziente Geräteplanung mit Multi-job-Föderierten Lernen | Soochow University | Aaai | 2022 | [Pub] [PDF] |
| Implizite Gradientenausrichtung im verteilten und föderierten Lernen | IIT Kanpur | Aaai | 2022 | [Pub] [PDF] |
| Klassifizierung der nächsten Nachbarn mit einer Kolonie von Obstfliegen | IBM -Forschung | Aaai | 2022 | [Pub] [PDF] [Code] |
| Iterierte Vektorfelder und Konservatismus mit Anwendungen für das Föderierte Lernen. | ALT | 2022 | [Pub] [PDF] | |
| Föderiertes Lernen mit sparsenfizierter Privatsphäre und adaptiver Optimierung | Ijcai | 2021 | [Pub] [PDF] [Video] | |
| Verhaltensmimikverteilung: Kombinieren des individuellen und Gruppenverhaltens für das Verbundlernen | Ijcai | 2021 | [Pub] [PDF] | |
| FedSpeech: Föderierte Text-zu-Sprache mit kontinuierlichem Lernen | Ijcai | 2021 | [Pub] [PDF] | |
| Praktisches One-Shot-Föderierte Lernen für Cross-Silo-Einstellung | Ijcai | 2021 | [Pub] [PDF] [Code] | |
| Föderierte Modelldestillation mit rauschfreier differenzierter Privatsphäre | Ijcai | 2021 | [Pub] [PDF] [Video] | |
| LDP-FL: Praktische private Aggregation im Föderierten Lernen mit lokaler Differential Privatsphäre | Ijcai | 2021 | [Pub] [PDF] | |
| Föderiertes Lernen mit fairer Mittelung. | Ijcai | 2021 | [Pub] [PDF] [Code] | |
| H-FL: Eine hierarchische Kommunikations- und Datenschutzarchitektur für das Föderierte Lernen. | Ijcai | 2021 | [Pub] [PDF] | |
| Kommunikationsbewertetes und skalierbares dezentrales Lernen der Verbundkante. | Ijcai | 2021 | [Pub] | |
| Sichere bilevel -asynchrone vertikale Föderierte Lernen mit rückwärtsgerechtigter Aktualisierung | Xidian University; JD Tech | Aaai | 2021 | [Pub] [PDF] [Video] |
| Fedrec ++: Verlustlose Empfehlung mit explizit | Szu | Aaai | 2021 | [Pub] [Video] |
| Verbund mehr bewaffnete Banditen | Universität von Virginia | Aaai | 2021 | [Pub] [PDF] [Code] [Video] |
| Über die Konvergenz von kommunikationseffizienten lokalen SGD für das Föderierte Lernen | Tempeluniversität; Universität von Pittsburgh | Aaai | 2021 | [Pub] [Video] |
| Flamme: Differenziell privates Föderierte Lernen im Shuffle -Modell | Renmin University of China; Universität Kyoto | Aaai | 2021 | [Pub] [PDF] [Video] [Code] |
| Um den Einfluss einzelner Kunden auf das Föderierte Lernen zu verstehen | Sjtu; Die Universität von Texas in Dallas | Aaai | 2021 | [Pub] [PDF] [Video] |
| Nachweislich sicheres Federated Learning gegen bösartige Kunden | Duke University | Aaai | 2021 | [Pub] [PDF] [Video] [Folie] |
| Personalisiertes Cross-Silo-Föderierte Lernen für Nicht-IID-Daten | Simon Fraser University; McMaster University | Aaai | 2021 | [Pub] [PDF] [Video] [UC.] |
| Modell-Sharing-Spiele: Analyse des Föderierten Lernens unter freiwilliger Beteiligung | Cornell-Universität | Aaai | 2021 | [Pub] [PDF] [Code] [Video] |
| Fluch oder Erlösung? Wie Datenheterogenität die Robustheit des Föderierten Lernens beeinflusst | Universität von Nevada; IBM -Forschung | Aaai | 2021 | [Pub] [PDF] [Video] |
| Gradientenspiel: Minderwerbung irrelevanter Kunden im Föderierten Lernen | IIT Bombay; IBM -Forschung | Aaai | 2021 | [Pub] [PDF] [Code] [Video] [Supp] |
| Föderierte Blockkoordinaten -Abstiegsschema zum Erlernen globaler und personalisierter Modelle | Cuhk; Arizona State University | Aaai | 2021 | [Pub] [PDF] [Video] [Code] |
| Behandeln des Ungleichgewichts des Unterrichts im Föderierten Lernen | Northwestern University | Aaai | 2021 | [Pub] [PDF] [Video] [Code] [解读] |
| Verteidigung gegen Hintertüren im Föderierten Lernen mit robuster Lernrate | Die Universität von Texas in Dallas | Aaai | 2021 | [Pub] [PDF] [Video] [Code] |
| Angriffe für Freireiter auf die Modellaggregation im Föderierten Lernen | Accenture Labs | Aistats | 2021 | [Pub] [PDF] [Code] [Video] [Supp] |
| Föderierte F-Differential Privatsphäre | Universität von Pennsylvania | Aistats | 2021 | [Pub] [Code] [Video] [Supp] |
| Föderiertes Lernen mit Komprimierung: Einheitliche Analyse und scharfe Garantien | Die Pennsylvania State University; Die Universität von Texas in Austin | Aistats | 2021 | [Pub] [PDF] [Code] [Video] [Supp] |
| Gemischtes Modell der unterschiedlichen Privatsphäre im Verbundlernen | UCLA; Google | Aistats | 2021 | [Pub] [Video] [Supp] |
| Kompromisse für Konvergenz und Genauigkeit in Federated Learning und Meta-Learning | Aistats | 2021 | [Pub] [PDF] [Video] [Supp] | |
| Verbund mehr bewaffnete Banditen mit Personalisierung | Universität von Virginia; Die Pennsylvania State University | Aistats | 2021 | [Pub] [PDF] [Code] [Video] [Supp] |
| Auf der Teilnahme flexibler Geräte am Federated Learning | CMU; Sysu | Aistats | 2021 | [Pub] [PDF] [Video] [Supp] |
| Föderierte Meta-Learning für betrügerische Kreditkartenerkennung | Ijcai | 2020 | [Pub] [Video] | |
| Ein Multispiel-Spiel zum Studium der Federated Learning Incentive-Programme | Ijcai | 2020 | [Pub] [Code] [解读] | |
| Praktische Federated Gradient Boosting -Entscheidungsbäume | Nus; Uwa | Aaai | 2020 | [Pub] [PDF] [Code] |
| Föderiertes Lernen für Seh- und Sprach-Erdungsprobleme | PKU; Tencent | Aaai | 2020 | [Pub] |
| Föderierte latente Dirichlet -Allokation: Ein lokaler differenzierter Privatsphäre basierender Rahmen | Buaa | Aaai | 2020 | [Pub] |
| Föderierte Patienthashing | Cornell-Universität | Aaai | 2020 | [Pub] |
| Robustes Föderierte Lernen über kollaborative Maschinenunterricht | Symantec Research Labs; KAUST | Aaai | 2020 | [Pub] [PDF] |
| FEDVISION: Eine Online -Plattform zur Erkennung visueller Objekte, die vom Federated Learning betrieben wird | Webank | Aaai | 2020 | [Pub] [PDF] [Code] |
| FEDPAQ: Eine kommunikationseffiziente Föderierten-Lernmethode mit periodischer Mittelung und Quantisierung | UC Santa Barbara; Ut Austin | Aistats | 2020 | [Pub] [PDF] [Video] [Supp] |
| Wie man föderiertes Lernen hinter die Tür setzt | Cornell Tech | Aistats | 2020 | [Pub] [PDF] [Video] [Code] [Supp] |
| Entdeckung von Federated Heavy Hitters mit unterschiedlicher Privatsphäre | RPI; Google | Aistats | 2020 | [Pub] [PDF] [Video] [Supp] |
| Multi-Agent-Visualisierung zur Erklärung des Föderierten Lernens | Webank | Ijcai | 2019 | [Pub] [Video] |
Federated Learning Papers, die von der Top -ML -Konferenz und Journal (Machine Learning) akzeptiert werden, einschließlich Neurips (Jahreskonferenz über neuronale Informationsverarbeitungssysteme), ICML (International Conference on Machine Learning), ICLR (International Conference on Learning Repräsentationen), Colt (jährliche Konferenz Computational Lerntheorie), UAI (Konferenz über Unsicherheit in der künstlichen Intelligenz), maschinelles Lernen, JMLR (Journal of Machine Learning Research), TPAMI (IEEE Transactions on Muster Analysis und Maschinell Intelligenz).
| Titel | Zugehörigkeit | Veranstaltungsort | Jahr | Materialien |
|---|---|---|---|---|
| Stabilisierung und Beschleunigung des Föderierten Lernens auf heterogenen Daten mit teilweise Kundenbeteiligung | Tpami | 2025 | [Pub] | |
| Medizinisches föderiertes Modell mit Mischung aus personalisierten und gemeinsamen Komponenten | Tpami | 2025 | [Pub] | |
| One-Shot-Föderierte Lernen durch synthetische Distiller-Distillatkommunikation | Neurips | 2024 | [Pub] | |
| Nicht konvexer Federated -Lernen auf kompakten glatten Submaniflolds mit heterogenen Daten | Neurips | 2024 | [Pub] | |
| FEDGMKD: Ein effizienter Prototyp Federated Learning Framework durch Wissensdestillation und Diskrepanzaggregation | Neurips | 2024 | [Pub] | |
| Verbesserung der Verallgemeinerung im Föderierten Lernen mit Modelldaten-Data-Data-Datenreduktion: Ein posteriorer Inferenzansatz | Neurips | 2024 | [Pub] | |
| Föderiertes Modell Heterogenes Matryoshka -Repräsentationslernen | Neurips | 2024 | [Pub] | |
| Federated Graph Learning for Cross-Domain-Empfehlung | Neurips | 2024 | [Pub] | |
| Fedgmark: zertifizierbar robustes Wasserzeichen für das Erlernen des Föderierten Diagramms | Neurips | 2024 | [Pub] | |
| Doppelpersonalisierungsadapter für föderierte Stiftungsmodelle | Neurips | 2024 | [Pub] | |
| Föderierte natürliche politische Gradienten- und Akteurkritikermethoden zum Verstärkungslernen mit mehreren Aufgaben | Neurips | 2024 | [Pub] | |
| Zähmung des langen Schwanzes bei der Vorhersage der menschlichen Mobilität | Neurips | 2024 | [Pub] | |
| Doppelverteidigung: Verbesserung der Privatsphäre und mildernde Vergiftungsangriffe im Föderierten Lernen | Neurips | 2024 | [Pub] | |
| Graph-verstärkte Optimierer für strukturbewusste Empfehlungen, die Evolution einbetten, | Neurips | 2024 | [Pub] | |
| Dofit: Domänenbewusster föderierte Unterrichtsstimmung mit gelindertem katastrophalem Vergessen | Neurips | 2024 | [Pub] | |
| Effizientes Föderierte Lernen gegen heterogene und nicht stationäre Klienten nicht verfügbar | Neurips | 2024 | [Pub] | |
| Federated Transformator: Mehrparteiener vertikaler Föderierte Lernen auf praktischen, unscharfen Daten | Neurips | 2024 | [Pub] | |
| Fiarse: Modell-heterogenes Lernen der Verbund durch Wichtigkeitsbewusstseins-Submodell-Extraktion | Neurips | 2024 | [Pub] | |
| Probabilistische Verbund ein Umfang mit nicht-iid- und unausgeglichenen Daten | Neurips | 2024 | [Pub] | |
| Flora: Föderierte Feinabstimmungsmodelle mit heterogenen Anpassungen mit niedrigem Rang | Neurips | 2024 | [Pub] | |
| Zähmung der Domänen-Repräsentationsvarianz im Föderierten Prototyp-Lernen mit heterogenen Datendomänen | Neurips | 2024 | [Pub] | |
| PFEDCLUB: kontrollierbare heterogene Modellaggregation für personalisiertes Federated Learning | Neurips | 2024 | [Pub] | |
| Warum voll werden? Erhöhen Sie das Lernen des Verbundes durch teilweise Netzwerkaktualisierungen | Neurips | 2024 | [Pub] | |
| Fusefl: One-Shot-Föderierte Lernen durch die Linse der Kausalität mit progressiver Modellfusion | Neurips | 2024 | [Pub] | |
| FEDSSP: Federated Graph Learning mit spektralem Wissen und personalisierte Präferenz | Neurips | 2024 | [Pub] | |
| Handling Learnwares aus heterogenen Merkmalsräumen mit explizitem Etikettnutzung | Neurips | 2024 | [Pub] | |
| A-FEDPD: Das Ausrichten von Dual-Drifts ist alles, was für primal-duale Lernbedürfnisse föderiert ist | Neurips | 2024 | [Pub] | |
| Private und personalisierte Frequenzschätzung in einer Verbundumgebung | Neurips | 2024 | [Pub] | |
| Der Komplexitätskompromiss der Probenkommunikationskomplexität im Föderierten Q-Learning | Neurips | 2024 | [Pub] | |
| Föderierte Ensemble-inszenierte Offline-Verstärkungslernen | Neurips | 2024 | [Pub] | |
| Federated Black-Box-Anpassung für die semantische Segmentierung | Neurips | 2024 | [Pub] | |
| Vorwärts denken: Speichereffizientes Federated-Finetuning von Sprachmodellen | Neurips | 2024 | [Pub] | |
| Föderiertes Lernen aus Vision-Sprage Foundation Models: Theoretische Analyse und Methode | Neurips | 2024 | [Pub] | |
| Optimales Design für die Erläuterung der menschlichen Präferenz | Neurips | 2024 | [Pub] | |
| Auf dem Weg zu vielfältigem Gerät Heterogenes Föderierten Lernen über Aufgaben arithmetische Wissensintegration | Neurips | 2024 | [Pub] | |
| Personalisiertes Föderierte Lernen über die Anpassung der Feature -Distribution | Neurips | 2024 | [Pub] | |
| Scafflsa: Zähmung der Heterogenität in der föderierten linearen stochastischen Approximation und TD -Lernen | Neurips | 2024 | [Pub] | |
| Ein Bayesian -Ansatz für das personalisierte Föderierte Lernen in heterogenen Umgebungen | Neurips | 2024 | [Pub] | |
| RFLPA: Ein robustes Verbund -Lernrahmen gegen Vergiftungsangriffe mit sicherer Aggregation | Neurips | 2024 | [Pub] | |
| FedGtst: Steigerung der globalen Übertragbarkeit von Federated -Modellen durch Statistikabstimmung | Neurips | 2024 | [Pub] | |
| End-to-End-Erkenntnisabteilung für Absichtserlernen in Empfehlung | Neurips | 2024 | [Pub] | |
| FEDLPA: One-Shot-Föderierte Lernen mit schichtweise posteriorer Aggregation | Neurips | 2024 | [Pub] | |
| Zeit-FFM: In Richtung LM-Empowered Federated Foundation-Modell für Zeitreihenprognosen | Neurips | 2024 | [Pub] | |
| FOOGD: Föderierte Zusammenarbeit sowohl für die Verallgemeinerung und Erkennung außerhalb der Verteilung | Neurips | 2024 | [Pub] | |
| Ein schweizerisches Armeemesser für das heterogene Föderierten Lernen: Flexible Kopplung über Trace -Norm | Neurips | 2024 | [Pub] | |
| Fedne: Ersatzunterstützte Nachbarn einbettet, um Dimensionalität zu reduzieren | Neurips | 2024 | [Pub] | |
| Niedrige präzise lokale Schulung reicht für das Föderierte Lernen aus | Neurips | 2024 | [Pub] | |
| Ressourcenbewusstes selbst überprüftes Lernen mit globalen Klassenpräsentationen | Neurips | 2024 | [Pub] | |
| Über die Notwendigkeit der Zusammenarbeit für die Online -Modellauswahl mit dezentralen Daten | Neurips | 2024 | [Pub] | |
| Die Kraft der Extrapolation im Verbundlernen | Neurips | 2024 | [Pub] | |
| . | Neurips | 2024 | [Pub] | |
| Zu Stichprobenstrategien für das Spektralmodell Sharding | Neurips | 2024 | [Pub] | |
| Anpassen von Sprachmodellen mit Instanz in Bezug auf LORA für sequentielle Empfehlungen | Neurips | 2024 | [Pub] | |
| SPAFL: Kommunikationswireffizientes Föderierte Lernen mit spärlichen Modellen und niedrigem Rechenaufwand | Neurips | 2024 | [Pub] | |
| Hydra-FL: Hybridwissensdestillation für robustes und genaues Föderierten Lernen | Neurips | 2024 | [Pub] | |
| Stabilisierte proximale Punktmethoden zur Verbundoptimierung | Neurips | 2024 | [Pub] | |
| Dapperfl: Domain Adaptive Federated Learning mit Modellfusions -Schnitt für Kantengeräte | Neurips | 2024 | [Pub] | |
| Parameterunterschiede Dissektion für die Hintertür -Verteidigung im heterogenen Föderierten Lernen | Neurips | 2024 | [Pub] | |
| Führt der schlimmste Agent das Paket? Analyse der Agentendynamik in einheitlicher verteilter SGD | Neurips | 2024 | [Pub] | |
| FEDAVP: Erweitern Sie die lokalen Daten über gemeinsame Richtlinien im Federated Learning | Neurips | 2024 | [Pub] | |
| COBO: Kollaboratives Lernen über Bilevel -Optimierung | Neurips | 2024 | [Pub] | |
| Konvergenzanalyse des geteilten Federated -Lernens auf heterogenen Daten | Neurips | 2024 | [Pub] | |
| Kommunikationsmitteleffizientes föderierte Gruppenverteilung verteilt robuste Optimierung | Neurips | 2024 | [Pub] | |
| Ferrari: Föderierte Funktion verlernen durch Optimierung der Funktionsempfindlichkeit | Neurips | 2024 | [Pub] | |
| Föderiertes Lernen über verbundene Modi | Neurips | 2024 | [Pub] | |
| Personalisiertes Föderierte Lernen mit Mischung von Modellen für adaptive Vorhersage und Modellfeinabwinne | Neurips | 2024 | [Pub] | |
| Führt die egalitäre Fairness zu Instabilität? Die Fairness grenzt im stabilen Verbund lernen unter altruistischen Verhaltensweisen | Neurips | 2024 | [Pub] | |
| Föderierte Online-Vorhersage von Experten mit unterschiedlicher Privatsphäre: Trennungen und Geschwindigkeit von Bedauern | Neurips | 2024 | [Pub] | |
| DataStaSealing: Daten aus Diffusionsmodellen im Verbundlernen mit mehreren Trojanern stehlen | Neurips | 2024 | [Pub] | |
| Verbundverhaltensebenen: Erklären Sie die Entwicklung des Kundenverhaltens im Föderierten Lernen | Neurips | 2024 | [Pub] | |
| Hierarchisch-Föderat-Lernen mit Multi-Times-Gradientenkorrektur | Neurips | 2024 | [Pub] | |
| Hyperprismus: Ein adaptives nichtlineares Aggregationsrahmen für verteilte maschinelle Lernen über nicht-iid-Daten und zeitvariable Kommunikationsverbindungen | Neurips | 2024 | [Pub] | |
| Speer: genaue Gradienteninversion von Chargen im Föderierten Lernen | Neurips | 2024 | [Pub] | |
| Föderiertes Lernen unter regelmäßiger Kundenbeteiligung und heterogenen Daten: Ein neuer kommunikationswirksamer Algorithmus und Analyse | Neurips | 2024 | [Pub] | |
| Bridging-Lücken: Verbindungsmulti-View-Clustering in heterogenen Hybridansichten | Neurips | 2024 | [Pub] | |
| Verwirrungsresistentes Föderierte Lernen durch Diffusionsbasis-Datenharmonisierung auf Nicht-IID-Daten | Neurips | 2024 | [Pub] | |
| Lokale übergeordnete Suppen: Ein Katalysator für das Modellverführen des Cross-Silo-Verbundlernens | Neurips | 2024 | [Pub] | |
| Die Bildung von Freilauf- und Konflikt-Bewusstsein für die Cross-Silo-Föderierte Lernen | Neurips | 2024 | [Pub] | |
| Klassifizierer -Clustering und Feature -Ausrichtung für das Federated Learning unter Distributed Concept Drift | Neurips | 2024 | [Pub] | |
| Heterogenitätsgesteuerte Client-Probenahme: Auf dem Weg zu einem schnellen und effizienten, nicht iid-Föderierten Lernen | Neurips | 2024 | [Pub] | |
| Fakt oder Fiktion: Können wahrheitsgemäße Mechanismen das föderierte freie Fahren beseitigen? | Neurips | 2024 | [Pub] | |
| Aktive Präferenzlernen für die Bestellung von Elementen in und aus der Stichprobe | Neurips | 2024 | [Pub] | |
| Föderierte Feinabstimmung von Großsprachmodellen unter heterogenen Aufgaben und Kundenressourcen | Neurips | 2024 | [Pub] | |
| Feinabstimmungspersonalisierung beim Föderierten Lernen zur Minderung von gegnerischen Kunden | Neurips | 2024 | [Pub] | |
| Überprüfung des Ensemblierens im One-Shot-Föderierten-Lernen | Neurips | 2024 | [Pub] | |
| Fedllm-Bench: Realistische Benchmarks für das Federated Lernen von großsprachigen Modellen | Neurips | 2024 | [Pub] | |
| $ exttt {pfl-ressearch} $: Simulationsrahmen für die Beschleunigung der Forschung im privaten Föderierten Lernen | Neurips | 2024 | [Pub] | |
| Fedmeki: Ein Maßstab für die skalierende medizinische Stiftungsmodelle über föderierte Wissensinjektion | Neurips | 2024 | [Pub] | |
| Impulsannäherung beim asynchronen privaten Federated Learning | Neurips Workshop | 2024 | [Pub] | |
| Kohortenquetsch | Neurips Workshop | 2024 | [Pub] | |
| Föderiertes Lernen mit generativem Inhalt | Neurips Workshop | 2024 | [Pub] | |
| Nutzung unstrukturierter Textdaten für die Stimmung der Föderierten Anweisungen großer Sprachmodelle | Neurips Workshop | 2024 | [Pub] | |
| Emerging Safety Attack und Verteidigung in der Federated -Unterrichtsabstimmung großer Sprachmodelle | Neurips Workshop | 2024 | [Pub] | |
| Überfassungsfreie Zusammenarbeit zwischen Wettbewerbern in einem Lernsystem | Neurips Workshop | 2024 | [Pub] | |
| Auf den Konvergenzraten des Föderierten Q-Learning in heterogenen Umgebungen | Neurips Workshop | 2024 | [Pub] | |
| Enkcluster: Die funktionale Verschlüsselung in föderierten Grundmodellen bringen | Neurips Workshop | 2024 | [Pub] | |
| Frettchen: Federated Vollparameter-Abstimmung im Maßstab für große Sprachmodelle | Neurips Workshop | 2024 | [Pub] | |
| Heißes Lernen von Steckböden föderiert | Neurips Workshop | 2024 | [Pub] | |
| Föderierte dynamische Schulungen mit niedrigem Rang mit globalen Verlustkonvergenzgarantien | Neurips Workshop | 2024 | [Pub] | |
| Die Zukunft des Großsprachenmodells vor der Ausbildung wird verstärkt | Neurips Workshop | 2024 | [Pub] | |
| Kollaboratives Lernen mit gemeinsamen linearen Darstellungen: statistische Raten und optimale Algorithmen | Neurips Workshop | 2024 | [Pub] | |
| Das Synapticcity -Phänomen: Wenn alle Stiftungsmodelle Federated Learning und Blockchain heiraten | Neurips Workshop | 2024 | [Pub] | |
| Zoopfl: Erkundung von Black-Box Foundation-Modellen für das persönliche Föderierte Lernen | Neurips Workshop | 2024 | [Pub] | |
| Decomfl: Federated Learning mit dimensionsfreier Kommunikation | Neurips Workshop | 2024 | [Pub] | |
| Verbesserung der Gruppenkonnektivität für die Verallgemeinerung des föderierten Deep Learning | Neurips Workshop | 2024 | [Pub] | |
| Karte: Modell, das mit amortisierter Pareto -Front mit begrenzter Berechnung zusammengeführt wird | Neurips Workshop | 2024 | [Pub] | |
| OPA: One-Shot Private Aggregation mit einzelnen Kundeninteraktion und ihren Anwendungen für das Föderierte Lernen | Neurips Workshop | 2024 | [Pub] | |
| Adaptive Hybridmodell -Beschneidung beim Föderierten Lernen durch Verlusterforschung | Neurips Workshop | 2024 | [Pub] | |
| Weltweit föderierte Ausbildung von Sprachmodellen | Neurips Workshop | 2024 | [Pub] | |
| Fedstein: Verbesserung des Lernens mit mehreren Domänen durch James-Stein-Schätzer | Neurips Workshop | 2024 | [Pub] | |
| Verbesserung der kausalen Entdeckung in föderierten Einstellungen mit begrenzten lokalen Proben | Neurips Workshop | 2024 | [Pub] | |
| $ exttt {pfl-ressearch} $: Simulationsrahmen für die Beschleunigung der Forschung im privaten Föderierten Lernen | Neurips Workshop | 2024 | [Pub] | |
| DMM: Verteilter Matrixmechanismus für differentiell private Föderierte Lernen mit gepacktem geheimen Teilen | Neurips Workshop | 2024 | [Pub] | |
| FEDCBO: Erreichung des Gruppenkonsens im Clustered Federated Learning durch konsensbasierte Optimierung | JMLR | 2024 | [Pub] | |
| Effektive Föderierte Grafikanpassung | ICML | 2024 | [Pub] | |
| Verständnis der serverunterstützten Föderierten in Anwesenheit einer unvollständigen Kundenbeteiligung | ICML | 2024 | [Pub] | |
| Jenseits der Föderation: Topologie-bewusstes Federated Learning für die Verallgemeinerung auf unsichtbare Kunden | ICML | 2024 | [Pub] | |
| FEDBPT: Effiziente Föderierte Black-Box-Eingabeaufforderung für große Sprachmodelle | ICML | 2024 | [Pub] | |
| Überbrückungsmodellheterogenität beim Föderierten Lernen durch unsicherheitsbasierte asymmetrische Reziprozitätslernen | ICML | 2024 | [Pub] | |
| Eine neue theoretische Perspektive auf die Datenheterogenität bei der Verbundoptimierung | ICML | 2024 | [Pub] | |
| Verbesserung der Speicherung und der Recheneffizienz im föderierten multimodalen Lernen für groß angelegte Modelle | ICML | 2024 | [] | |
| Dynamik für den Sieg: Kollaborative Föderierte Verstärkungslernen in heterogenen Umgebungen | ICML | 2024 | [Pub] | |
| Byzantine-Robust Federated Learning: Auswirkungen von Kunden-Subsampling und lokalen Updates | ICML | 2024 | [Pub] | |
| Nachweisbare Vorteile lokaler Schritte im heterogenen Föderierten Lernen für neuronale Netzwerke: Eine Perspektive für das Lernen von Merkmalen | ICML | 2024 | [Pub] | |
| Beschleunigungsbeschleunigter Federated Learning mit schnell verteilter mittlerer Schätzung | ICML | 2024 | [Pub] | |
| Fair Federated Lernen über den proportionalen Veto -Kern | ICML | 2024 | [Pub] | |
| AEGISFL: Effizientes und flexibler Privatsphäre, die byzantinische Robust-Cross-Silo-Föderierte lernte | ICML | 2024 | [Pub] | |
| Wiederherstellung von Etiketten aus lokalen Updates im Föderierten Learning | ICML | 2024 | [Pub] | |
| Fedmbridge: Brückenbares multimodales Föderierten Lernen | ICML | 2024 | [Pub] | |
| Harmonisierung der Verallgemeinerung und Personalisierung im föderierten schnellen Lernen | ICML | 2024 | [Pub] | |
| Lokal geschätzte globale Störungen sind besser als lokale Störungen für die minimierende minimierende Verbundschärfe. | ICML | 2024 | [Pub] | |
| Beschleunigende heterogene Föderierten mit geschlossenen Klassifizierern | ICML | 2024 | [Pub] | |
| Föderierte kombinatorische Mehr-Agent-Multi-bewaffnete Banditen | ICML | 2024 | [Pub] | |
| Eine doppelt rekursive stochastische Zusammensetzung der Zusammensetzung der Zusammensetzung für die föderierte Zusammensetzung | ICML | 2024 | [Pub] | |
| Private heterogene Föderierte Lernen ohne vertrauenswürdige Server: Fehleroptimale und kommunikomikum-effiziente Algorithmen für konvexe Verluste | ICML | 2024 | [Pub] | |
| FEDRC: Bekämpfung der Herausforderung für Verteilungsverschiebungen im Föderierten Lernen durch robustes Clustering | ICML | 2024 | [Pub] | |
| Verfolgung des allgemeinen Wohlergehens im Föderierten Lernen durch sequentielle Entscheidungsfindung | ICML | 2024 | [Pub] | |
| Vor-Text: Schulungssprachmodelle für private föderierte Daten im Zeitalter von LLMs | ICML | 2024 | [Pub] | |
| Selbstgetriebene Entropie-Aggregation für byzantinisch-robustes heterogenes Föderierten Lernen | ICML | 2024 | [Pub] | |
| Überwindung von Daten und Modellheterogenitäten im dezentralen Verbundlernen über synthetische Anker | ICML | 2024 | [Pub] | |
| Föderierte Optimierung mit doppelt regulierter Driftkorrektur | ICML | 2024 | [Pub] | |
| FEDSC: Nachweisbares selbst übertriebenes Lernen mit spektralem kontrastivem Ziel über nicht-iid-Daten | ICML | 2024 | [Pub] | |
| Konforme Vorhersage von byzantinisch-robustverbundem Föderierte | ICML | 2024 | [Pub] | |
| Erzielen Sie eine verlustfreie Gradienten -Sparifizierung durch Kartierung auf alternativen Raum im Föderierten Lernen | ICML | 2024 | [Pub] | |
| Clustered Federated Learning durch gradientenbasierte Partitionierung | ICML | 2024 | [Pub] | |
| Wiederkehrende frühe Ausgänge für das Föderierte Lernen mit heterogenen Kunden | ICML | 2024 | [Pub] | |
| Überdenken Sie die flache Minima, die im Föderierten Lernen sucht | ICML | 2024 | [Pub] | |
| Fedbat: Kommunikationsbewertetes Föderierte Lernen durch lernbare Binarisierung | ICML | 2024 | [Pub] | |
| Federated Repräsentation Lernen im unter parametrisierten Regime | ICML | 2024 | [Pub] | |
| FEDLMT: Angriffssystem Heterogenität des Föderierten Lernens durch niedriges Modelltraining mit theoretischen Garantien | ICML | 2024 | [Pub] | |
| Lärmbewusster Algorithmus für heterogenes Differentiell Private Federated Learning | ICML | 2024 | [Pub] | |
| Silber: Einschließungsvarianz Reduzierung und Anwendung auf das Föderierte Lernen | ICML | 2024 | [Pub] | |
| SignSGD mit Verbesserungen der Verbesserung: Gegnerangriffe durch Gradientenzeichen -Dekodierung nutzen | ICML | 2024 | [Pub] | |
| FedCal: Erreichen lokaler und globaler Kalibrierung im Verbundlernen über aggregierte parametrisierte Skalier | ICML | 2024 | [Pub] | |
| Föderiertes kontinuierliches Lernen durch prompt-basierte Dual-Wissensübertragung | ICML | 2024 | [Pub] | |
| Föderierte Vollparameter-Abstimmung von milliardengroßen Sprachmodellen mit Kommunikationskosten unter 18 Kilobyten | ICML | 2024 | [Pub] | |
| Dezentierbare submoduläre Maximierung in der föderierten Einstellung | ICML | 2024 | [Pub] | |
| Private und föderierte stochastische konvexe Optimierung: Effiziente Strategien für zentralisierte Systeme | ICML | 2024 | [Pub] | |
| Verbesserte Modellierung von föderierten Datensätzen unter Verwendung von Mischungen von Dirichlet-Multinomen | ICML | 2024 | [Pub] | |
| Lehren aus der Verallgemeinerungsfehleranalyse des Federated Learning: Sie können weniger oft kommunizieren! | ICML | 2024 | [Pub] | |
| Byzantinisch belastbar und schnell föderiertes Lernen mit wenigen Schichten | ICML | 2024 | [Pub] | |
| Kausal motiviertes personalisiertes föderiertes invariant | ICML | 2024 | [Pub] | |
| Ranking-basierte Kunden-Imitationsauswahl für Kunden für ein effizientes Verbundlernen | ICML | 2024 | [Pub] | |
| Auf dem Weg zur Theorie des unbeaufsichtigten Federated Learning: Nicht-asymptotische Analyse der föderierten EM-Algorithmen | ICML | 2024 | [Pub] | |
| FADAS: Auf dem Weg zu föderierten adaptiven asynchronen Optimierung | ICML | 2024 | [Pub] | |
| Föderierte Offline-Verstärkungslernen: Kollaborative Einpolitikberichterstattung reicht aus | ICML | 2024 | [Pub] | |
| FedRedEfense: Verteidigung gegen Modellvergiftungangriffe für das Föderierte Lernen unter Verwendung des Rekonstruktionsfehlers der Modellaktualisierung | ICML | 2024 | [Pub] | |
| MH-PFLID: Modell Heterogenes personalisiertes föderiertes Lernen durch Injektion und Destillation für die Analyse der medizinischen Daten | ICML | 2024 | [Pub] | |
| Föderierte neurosymbolische Lernen | ICML | 2024 | [Pub] | |
| Anpassungsgruppe Personalisierung für das Lernen von Federated Mutual Transfer | ICML | 2024 | [Pub] | |
| Ausgleiche der Ähnlichkeit und Komplementarität für das Föderierten Lernen auszugleichen | ICML | 2024 | [Pub] | |
| Verbesserte selbsterklärende GNNs mit Anti-Shortcut-Augmentationen | ICML | 2024 | [Pub] | |
| Ein föderatter stochastischer multi-Level-Zusammensetzungsminimatalgorithmus für die tiefe AUC-Maximierung | ICML | 2024 | [Pub] | |
| Coala: Eine praktische und visionorientierte Lernplattform, die föderierte Lernplattform | ICML | 2024 | [Pub] | |
| Sicheres und schnelles asynchrones vertikales Federated -Lernen durch kaskadierte Hybridoptimierung | Mach lerne | 2024 | [Pub] | |
| Kommunikationswireeffizientes Clustered Federated Learning über Modellentfernung | Ustc; Staates Schlüssellabor für kognitive Intelligenz | Mach lerne | 2024 | [Pub] |
| Föderiertes Lernen mit überquantiler Aggregation für heterogene Daten. | Google -Forschung | Mach lerne | 2024 | [Pub] [PDF] [Code] |
| Ausrichtungsmodellausgänge für die Klassenausgleichung nicht iid-Föderierte Lernen | NJU | Mach lerne | 2024 | [Pub] |
| Föderiertes Lernen von verallgemeinerten linearen kausalen Netzwerken | Tpami | 2024 | [Pub] | |
| Cross-Modal Federated Human Activity Anerkennung | Tpami | 2024 | [Pub] | |
| Föderatter Gaußscher Prozess: Konvergenz, automatische Personalisierung und Mehrfach-Fidel-Modellierung | Nordöstliche Universität; Uom | Tpami | 2024 | [Pub] [PDF] [Code] |
| Die Auswirkungen von gegnerischen Angriffen auf das Föderierte Lernen: eine Umfrage | ICH S | Tpami | 2024 | [Pub] |
| Verständnis und mildern dimensionaler Zusammenbruch beim Föderierten Lernen | Nus | Tpami | 2024 | [Pub] [PDF] [Code] |
| Niemand zurückgelassen: Real World Federated Class-Incremental Lernen | CAS; UCAs | Tpami | 2024 | [Pub] [PDF] [Code] |
| Verallgemeinerbare heterogene Föderierte Kreuzkorrelation und Instanz-Ähnlichkeitslernen | WHU | Tpami | 2024 | [Pub] [PDF] [Code] |
| Mehrstufiger asynchrones Föderierten Lernen mit adaptiver Differential Privatsphäre | HPU; Xjtu | Tpami | 2024 | [Pub] [PDF] [Code] |
| Ein Bayesian Federated Learning Framework mit Online -Laplace -Annäherung | Sustech | Tpami | 2024 | [Pub] [PDF] [Code] |
| Verbesserung des One-Shot-Verbundlernens durch Daten- und Ensemble-Co-Boosting | Ustc; HKBU | ICLR | 2024 | [Pub] |
| Ein-Schuss empirische Datenschutzschätzung für das Föderierte Lernen | ICLR | 2024 | [Pub] [PDF] | |
| Stochastische kontrollierte Mittelung für das Föderierte Lernen mit Kommunikationskomprimierung | LinkedIn; Upenn | ICLR | 2024 | [Pub] [PDF] |
| Eine leichte Methode zur Bekämpfung unbekannter Beteiligungsstatistiken bei der Mittelung der Föderation | IBM | ICLR | 2024 | [Pub] [PDF] [Code] |
| Eine gegenseitige Informationsperspektive auf das föderierte kontrastive Lernen | Qualcomm | ICLR | 2024 | [Pub] |
| Benchmarking -Algorithmen für die Verallgemeinerung der Föderierten Domänen | Purdue University | ICLR | 2024 | [Pub] [PDF] [Code] |
| Effektives und effizientes Föderat -Baumlernen für Hybriddaten | UC Berkeley | ICLR | 2024 | [Pub] [PDF] |
| Föderierte Empfehlung mit additiver Personalisierung | UTS | ICLR | 2024 | [Pub] [PDF] [Code] |
| Bekämpfung der Datenheterogenität im asynchronen Föderierten Lernen mit zwischengespeicherter Aktualisierungskalibrierung | PSU | ICLR | 2024 | [Pub] [Supp] |
| Föderierte orthogonale Ausbildung: Minderung des globalen katastrophalen Vergessens beim kontinuierlichen Föderierten Lernen | USC | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Genaues Vergessen für heterogenes föderiertes kontinuierliches Lernen | DO | ICLR | 2024 | [Pub] [Code] |
| Föderierte kausale Entdeckung aus heterogenen Daten | Mbzuai | ICLR | 2024 | [Pub] [PDF] [Code] |
| Auf differentiell private föderierte lineare kontextbezogene Banditen | Wayne State University | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Anreize wahrheitsgemäße Kommunikation für Föderierte Banditen | Universität von Virginia | ICLR | 2024 | [Pub] [PDF] |
| Prinzipielle Anpassung der Föderation Domänen: Gradientenprojektion und automatische Gewicht | UIUC | ICLR | 2024 | [Pub] |
| FEDP3: Föderierte personalisierte und Privatsphäre-freundliche Netzwerkbeschnitte unter Modellheterogenität | KAUST | ICLR | 2024 | [Pub] |
| Textgetriebene Eingabeaufforderung Generation für Visionsprachel-Modelle im Föderierten Lernen | Robert Bosch LLC | ICLR | 2024 | [Pub] [PDF] |
| Verbesserung der Lora im Datenschutzgebiet Federated Learning | Northeastern University | ICLR | 2024 | [Pub] |
| Fedwon: Triumphieren von Multi-Domain-Föderierten ohne Normalisierung | Sony Ai | ICLR | 2024 | [Pub] [PDF] |
| FEDTRANS: Kundendrandtransparente Versorgungsschätzung für das robuste Lernen der Verbundwerte | Tu Delft | ICLR | 2024 | [Pub] |
| FedCompass: Effizientes Cross-Silo-Föderierte Lernen auf heterogenen Client-Geräten unter Verwendung eines Computer-Power-Award-Schedulers | ANL; UIUC; NCSA | ICLR | 2024 | [Pub] [PDF] [Code] [Seite] |
| Bayes'sche Coreset -Optimierung für das Lernen des personalisierten Verbundes | IIT Bombay | ICLR | 2024 | [Pub] |
| Ebene die Konnektivität des linearen Modus-Modus | Ruhr-Unerstät Bochum | ICLR | 2024 | [Pub] [PDF] [Supp] |
| Fälschen Sie es bis zum Machen Sie es: Föderiertes Lernen mit konsensorientierter Generation | Sjtu | ICLR | 2024 | [Pub] [PDF] |
| Versteck in Sichtweite: Verschleiern Sie Daten, die Angriffe im Verbundgelern stehlen | Insait | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Finite-Zeit | Columbia University | ICLR | 2024 | [Pub] [PDF] |
| Adaptive Federated Learning mit automatisch abgestimmten Kunden | Rice University | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Lernen des Backdoor Federated durch Vergiftung durch Hintertür-kritische Schichten | Nd | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Föderierte Q-Learning: Lineares Bedauern beschleunigt mit niedrigen Kommunikationskosten | PSU | ICLR | 2024 | [Pub] [Supp] [PDF] |
| FedimPro: Messung und Verbesserung des Kunden -Update im Föderierten Lernen | HKBU | ICLR | 2024 | [Pub] [PDF] |
| Föderierte Wasserstein -Entfernung | MIT | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Eine verbesserte Analyse von Pro-Probe und Pro-Update-Ausschnitten im Verbundlernen | DTU | ICLR | 2024 | [Pub] |
| FedCDA: Föderiertes Lernen mit Kreuzrunden-Divergenz-bewusstes Aggregation | NTU | ICLR | 2024 | [Pub] [Supp] |
| Interne Verstärkergradienten für die Ausweitung der Homogenität auf Heterogenität beim Verbundern | HKU | ICLR | 2024 | [Pub] [PDF] |
| Impuls profitiert das nicht-iid-Verbund-Lernen einfach und nachweislich vorstellbar | PKU; Upenn | ICLR | 2024 | [Pub] [PDF] |
| Kommunikationseffizientes föderiertes nichtlineares Bandit-Optimierung | Yale-Universität | ICLR | 2024 | [Pub] [PDF] |
| Faire und effiziente Beitragsbewertung für vertikale Föderationslernen | Huawei | ICLR | 2024 | [PUB] [SUPP] [PDF] [Code] |
| Entmystifizierende lokale und globale Fairness-Kompromisse beim Verbund von Lernen unter Verwendung einer Teilinformationsabteilung | UMCP | ICLR | 2024 | [Pub] [PDF] |
| Erlernen personalisierter kausalinvarianter Darstellungen für heterogene Verbundkunden | Polyu | ICLR | 2024 | [Pub] |
| PEFLL: Personalisiertes Federated Learning durch Lernen zum Lernen | IST | ICLR | 2024 | [Pub] [Supp] [PDF] |
| Kommunikationswireeffiziente Gradientenabfälleaketentum für verteilte Variationsungleichheiten: Einheitliche Analyse und lokale Aktualisierungen | JHU | ICLR | 2024 | [Pub] [Supp] [PDF] |
| FedInverse: Evaluating Privacy Leakage in Federated Learning | USQ | ICLR | 2024 | [PUB] [SUPP] |
| FedDA: Faster Adaptive Gradient Methods for Federated Constrained Optimization | UMCP | ICLR | 2024 | [PUB] [SUPP] [PDF] |
| Robust Training of Federated Models with Extremely Label Deficiency | HKBU | ICLR | 2024 | [PUB] [PDF] [CODE] |
| Understanding Convergence and Generalization in Federated Learning through Feature Learning Theory | RIKEN AIP | ICLR | 2024 | [PUB] |
| Teach LLMs to Phish: Stealing Private Information from Language Models | Princeton University | ICLR | 2024 | [PUB] |
| Like Oil and Water: Group Robustness Methods and Poisoning Defenses Don't Mix | UMCP | ICLR | 2024 | [PUB] |
| Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise | HKUST | ICLR | 2024 | [PUB] [PDF] |
| Towards Eliminating Hard Label Constraints in Gradient Inversion Attacks | CAS | ICLR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| Local Composite Saddle Point Optimization | Purdue University | ICLR | 2024 | [PUB] [PDF] |
| Enhancing Neural Training via a Correlated Dynamics Model | TIIT | ICLR | 2024 | [PUB] [PDF] |
| EControl: Fast Distributed Optimization with Compression and Error Control | Saarland University | ICLR | 2024 | [PUB] [SUPP] [PDF] |
| Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit | HKUST | ICLR | 2024 | [PUB] |
| FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent | UMCP | ICLR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| Heterogeneous Personalized Federated Learning by Local-Global Updates Mixing via Convergence Rate | CUHK | ICLR | 2024 | [PUB] |
| Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages | Universität Cambridge | ICLR | 2024 | [PUB] |
| Simple Minimax Optimal Byzantine Robust Algorithm for Nonconvex Objectives with Uniform Gradient Heterogeneity | NTT DATA Mathematical Systems Inc. | ICLR | 2024 | [PUB] |
| VFLAIR: A Research Library and Benchmark for Vertical Federated Learning | DO | ICLR | 2024 | [PUB] [PDF] [CODE] |
| Incentive-Aware Federated Learning with Training-Time Model Rewards | NUS | ICLR | 2024 | [PUB] [SUPP] |
| VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks | NUS | ICLR | 2024 | [PUB] [PDF] [CODE] |
| FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data | ZJU | ICLR | 2024 | [PUB] [SUPP] [PDF] |
| SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning | University at Buffalo | NeurIPS | 2023 | [PUB] [PDF] [SUPP] |
| Mechanism Design for Collaborative Normal Mean Estimation | UW-Madison | NeurIPS | 2023 | [PUB] [PDF] |
| Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity | EPFL | NeurIPS | 2023 | [PUB] [PDF] [CODE] |
| Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization | UIUC | NeurIPS | 2023 | [PUB] [SUPP] |
| Convergence Analysis of Sequential Federated Learning on Heterogeneous Data | BUPT | NeurIPS | 2023 | [PUB] [PDF] [CODE] |
| Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition | MBZUAI | NeurIPS | 2023 | [PUB] [PDF] [CODE] |
| Private Federated Frequency Estimation: Adapting to the Hardness of the Instance | JHU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization | Rutgers University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Incentivized Communication for Federated Bandits | University of Virginia | NeurIPS | 2023 | [PUB] [PDF] |
| Multiply Robust Federated Estimation of Targeted Average Treatment Effects | Northeastern University | NeurIPS | 2023 | [PUB] [PDF] |
| IBA: Towards Irreversible Backdoor Attacks in Federated Learning | Vanderbilt University; VinUniversity | NeurIPS | 2023 | [PUB] [SUPP] [CODE] |
| EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning | KAIST | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Federated Linear Bandits with Finite Adversarial Actions | University of Virginia | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| FedNAR: Federated Optimization with Normalized Annealing Regularization | MBZUAI | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Guiding The Last Layer in Federated Learning with Pre-Trained Models | Concordia University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization | HZAU | NeurIPS | 2023 | [PUB] [SUPP] |
| Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Approach for Object Detection | KAIST | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks | USC | NeurIPS | 2023 | [PUB] [PDF] [CODE] |
| Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning | UTS | NeurIPS | 2023 | [PUB] [SUPP] |
| One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning | Rice University | NeurIPS | 2023 | [PUB] [SUPP] [CODE] |
| Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training | Gatech | NeurIPS | 2023 | [PUB] [SUPP] [CODE] |
| FedGame: A Game-Theoretic Defense against Backdoor Attacks in Federated Learning | PSU; UIUC | NeurIPS | 2023 | [PUB] [SUPP] [CODE] |
| Towards Personalized Federated Learning via Heterogeneous Model Reassembly | PSU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction | GWU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning | ECNU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| A Unified Solution for Privacy and Communication Efficiency in Vertical Federated Learning | Western University | NeurIPS | 2023 | [PUB] [SUPP] [CODE] |
| RECESS Vaccine for Federated Learning: Proactive Defense Against Model Poisoning Attacks | Xidian University; University of Guelph; Zhejiang Key Laboratory of Multi-dimensional Perception Technology, Application and Cybersecurity | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Federated Learning with Bilateral Curation for Partially Class-Disjoint Data | SJTU; Shanghai AI Laboratory | NeurIPS | 2023 | [PUB] [SUPP] [CODE] |
| Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds | GMU; SJTU | NeurIPS | 2023 | [PUB] [SUPP] [CODE] |
| FedL2P: Federated Learning to Personalize | University of Cambridge; Samsung AI Center | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Adaptive Test-Time Personalization for Federated Learning | UIUC | NeurIPS | 2023 | [PUB] [PDF] [CODE] |
| Federated Conditional Stochastic Optimization | University of Pittsburgh | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Federated Spectral Clustering via Secure Similarity Reconstruction | CUHK | NeurIPS | 2023 | [PUB] |
| Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM | UM-Dearborn | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks | CMU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Federated Multi-Objective Learning | RIT | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout | University of British Columbia; Gatech | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Resolving the Tug-of-War: A Separation of Communication and Learning in Federated Learning | University of Pittsburgh | NeurIPS | 2023 | [PUB] [SUPP] |
| Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems | University of Pittsburgh | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| StableFDG: Style and Attention Based Learning for Federated Domain Generalization | KAIST; Purdue University | NeurIPS | 2023 | [PUB] [PDF] |
| Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization | 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 | BISSCHEN | 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 | University of Pittsburgh | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Federated Learning via Meta-Variational Dropout | SNU | NeurIPS | 2023 | [PUB] [CODE] |
| Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates | Purdue University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| SPACE: Single-round Participant Amalgamation for Contribution Evaluation in Federated Learning | NTU | NeurIPS | 2023 | [PUB] [CODE] |
| Fed-FA: Theoretically Modeling Client Data Divergence for Federated Language Backdoor Defense | PKU; Tencent | NeurIPS | 2023 | [PUB] [SUPP] |
| FedFed: Feature Distillation against Data Heterogeneity in Federated Learning | BUAA; HKBU | NeurIPS | 2023 | [PUB] [PDF] [CODE] |
| PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning | SCU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] [解读] |
| Spectral Co-Distillation for Personalized Federated Learning | SUTD | NeurIPS | 2023 | [PUB] |
| Breaking the Communication-Privacy-Accuracy Tradeoff with | ZJU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation | Stanford University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| (Amplified) Banded Matrix Factorization: A unified approach to private training | Google DeepMind | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices | KIT | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation | Stanford University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Incentivizing Honesty among Competitors in Collaborative Learning and Optimization | ETH Zurich | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Resilient Constrained Learning | UPenn | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting | KAUST | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Collaboratively Learning Linear Models with Structured Missing Data | Stanford University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy | EPFL | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Fast Optimal Locally Private Mean Estimation via Random Projections | Apple Inc. | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Contextual Stochastic Bilevel Optimization | EPFL; ETH Zürich | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Understanding Deep Gradient Leakage via Inversion Influence Functions | MSU; Michigan State University; University of Texas at Austin | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Inner Product-based Neural Network Similarity | Purdue University | NeurIPS | 2023 | [PUB] [SUPP] |
| Correlation Aware Sparsified Mean Estimation Using Random Projection | CMU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| TIES-Merging: Resolving Interference When Merging Models | UNC | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data | Purdue University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Large-Scale Distributed Learning via Private On-Device LSH | UMD | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Faster Relative Entropy Coding with Greedy Rejection Coding | Universität 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 | FOHLEN | 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-Universität | 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 | Alibaba Group | 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 | University at Buffalo | ICML | 2023 | [PUB] [PDF] |
| Towards Understanding Ensemble Distillation in Federated Learning | KAIST | ICML | 2023 | [PUB] |
| Personalized Subgraph Federated Learning | KAIST | ICML | 2023 | [PUB] [PDF] [CODE] |
| Conformal Prediction for Federated Uncertainty Quantification Under Label Shift | Lagrange Mathematics and Computing Research Center; CMAP | ICML | 2023 | [PUB] [PDF] |
| Secure Federated Correlation Test and Entropy Estimation | CMU | ICML | 2023 | [PUB] [PDF] |
| Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships | JLU | ICML | 2023 | [PUB] [CODE] |
| Personalized Federated Learning under Mixture of Distributions | UCLA | ICML | 2023 | [PUB] [PDF] [CODE] |
| FedDisco: Federated Learning with Discrepancy-Aware Collaboration | SJTU | ICML | 2023 | [PUB] [PDF] [CODE] |
| Anchor Sampling for Federated Learning with Partial Client Participation | Purdue University | ICML | 2023 | [PUB] [PDF] [CODE] |
| Private Federated Learning with Autotuned Compression | JHU; Google | ICML | 2023 | [PUB] [PDF] |
| Fast Federated Machine Unlearning with Nonlinear Functional Theory | Auburn-Universität | 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 | SCHLAG | ICML | 2023 | [PUB] [CODE] |
| Personalized Federated Learning with Inferred Collaboration Graphs | SJTU | ICML | 2023 | [PUB] [CODE] |
| Optimizing the Collaboration Structure in Cross-Silo Federated Learning | UIUC | ICML | 2023 | [PUB] [PDF] [CODE] [SLIDES] |
| TabLeak: Tabular Data Leakage in Federated Learning | ETH Zurich | ICML | 2023 | [PUB] [PDF] [CODE] |
| FedCR: Personalized Federated Learning Based on Across-Client Common Representation with Conditional Mutual Information Regularization | SJTU | ICML | 2023 | [PUB] [CODE] |
| Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction | Duke University | ICML | 2023 | [PUB] [PDF] |
| Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design | Meta AI | 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 | DO | ICML | 2023 | [PUB] [PDF] |
| Efficient Personalized Federated Learning via Sparse Model-Adaptation | Alibaba Group | ICML | 2023 | [PUB] [PDF] [CODE] |
| From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning | Univ. Lille | ICML | 2023 | [PUB] [PDF] [CODE] |
| LeadFL: Client Self-Defense against Model Poisoning in Federated Learning | TUD | ICML | 2023 | [PUB] [CODE] |
| Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning | HKUST | ICML | 2023 | [PUB] [PDF] [CODE] |
| FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models | HKUST | ICML | 2023 | [PUB] [PDF] |
| FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction | CUHK; The Shenzhen Institute of Artificial Intelligence and Robotics for Society | ICML | 2023 | [PUB] [PDF] [CODE] |
| Towards Unbiased Training in Federated Open-world Semi-supervised Learning | PolyU | ICML | 2023 | [PUB] [PDF] [SLIDES] |
| Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis | Georgia Tech; Meta AI | ICML | 2023 | [PUB] [PDF] |
| Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning | KU Leuven | ICML | 2023 | [PUB] [PDF] [CODE] |
| Fair yet Asymptotically Equal Collaborative Learning | NUS | ICML | 2023 | [PUB] [PDF] [CODE] |
| Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability | Adobe Research | ICML | 2023 | [PUB] [PDF] |
| Adversarial Collaborative Learning on Non-IID Features | UC Berkeley; NUS | ICML | 2023 | [PUB] |
| XTab: Cross-table Pretraining for Tabular Transformers | EPFL; Cornell University; AWS | ICML | 2023 | [PUB] [PDF] [CODE] |
| Momentum Ensures Convergence of SIGNSGD under Weaker Assumptions | NUDT | ICML | 2023 | [PUB] |
| Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting | Key Lab of Intelligent Computing Based Big Data of Zhejiang Province; ZJU; Sony Al | ICML | 2023 | [PUB] [PDF] [CODE] |
| LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning | Rensselaer Polytechnic Institute | ICML | 2023 | [PUB] [PDF] |
| FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks | University of Minnesota | ICML | 2023 | [PUB] |
| Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm | Universität von 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 | Universität 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 | DO | 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 | Universität Cambridge | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Multimodal Federated Learning via Contrastive Representation Ensemble | DO | 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 | Universität Cambridge | ICLR | 2023 | [PUB] [PDF] |
| Does Decentralized Learning with Non-IID Unlabeled Data Benefit from Self Supervision? | MIT | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top | mbzuai | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Dual Diffusion Implicit Bridges for Image-to-Image Translation | Stanford | ICLR | 2023 | [PUB] [PDF] [CODE] |
| An accurate, scalable and verifiable protocol for federated differentially private averaging | INRIA Lille | Mach Learn | 2022 | [PUB] [PDF] |
| Federated online clustering of bandits. | CUHK | UAI | 2022 | [PUB] [PDF] [CODE] |
| Privacy-aware compression for federated data analysis. | Meta AI | 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 | DO | 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 | Amazonas | NeurIPS | 2022 | [PUB] [PDF] |
| A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning | Northeastern University | NeurIPS | 2022 | [PUB] [PDF] |
| An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects | NUS | NeurIPS | 2022 | [PUB] |
| Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning | EPFL | NeurIPS | 2022 | [PUB] [PDF] |
| Personalized Online Federated Multi-Kernel Learning | UCI | NeurIPS | 2022 | [PUB] |
| SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training | Duke University | NeurIPS | 2022 | [PUB] [PDF] [CODE] |
| A Unified Analysis of Federated Learning with Arbitrary Client Participation | IBM | NeurIPS | 2022 | [PUB] [PDF] |
| Preservation of the Global Knowledge by Not-True Distillation in Federated Learning | KAIST | NeurIPS | 2022 | [PUB] [PDF] [CODE] |
| FedSR: A Simple and Effective Domain Generalization Method for Federated Learning | University of Oxford | NeurIPS | 2022 | [PUB] [CODE] |
| Factorized-FL: Personalized Federated Learning with Parameter Factorization & Similarity Matching | KAIST | NeurIPS | 2022 | [PUB] [PDF] [CODE] |
| A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits | UC | NeurIPS | 2022 | [PUB] [PDF] |
| Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework | Tulane-Universität | NeurIPS | 2022 | [PUB] |
| On Privacy and Personalization in Cross-Silo Federated Learning | CMU | NeurIPS | 2022 | [PUB] [PDF] |
| A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning | NUS | NeurIPS | 2022 | [PUB] [PDF] [CODE] |
| FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings | Owkin | NeurIPS Datasets and Benchmarks | 2022 | [PUB] [CODE] |
| A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources | University of Pittsburgh | ICML | 2022 | [PUB] [PDF] [CODE] |
| Fast Composite Optimization and Statistical Recovery in Federated Learning | SJTU | ICML | 2022 | [PUB] [PDF] [CODE] |
| Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning | NYU | ICML | 2022 | [PUB] [PDF] [CODE] |
| The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning | Stanford; 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 | Universität Cambridge | ICML | 2022 | [PUB] [PDF] [SLIDE] [CODE] |
| Accelerated Federated Learning with Decoupled Adaptive Optimization | Auburn-Universität | ICML | 2022 | [PUB] [PDF] |
| Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling | Georgia Tech | ICML | 2022 | [PUB] [PDF] |
| Multi-Level Branched Regularization for Federated Learning | Seoul National University | ICML | 2022 | [PUB] [PDF] [CODE] [PAGE] |
| FedScale: Benchmarking Model and System Performance of Federated Learning at Scale | Universität von 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 | Universität von 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 | Universität von Michigan | ICML | 2022 | [PUB] [PDF] [CODE] |
| EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning | VMware Research | ICML | 2022 | [PUB] [PDF] [CODE] |
| Communication-Efficient Adaptive Federated Learning | Pennsylvania State University | ICML | 2022 | [PUB] [PDF] |
| ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training | CISPA Helmholz Center for Information Security | ICML | 2022 | [PUB] [PDF] [SLIDE] [CODE] |
| Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification | University of Maryland | ICML | 2022 | [PUB] [PDF] [CODE] |
| Anarchic Federated Learning | The Ohio State University | ICML | 2022 | [PUB] [PDF] |
| QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning | Nankai University | ICML | 2022 | [PUB] [CODE] |
| Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization | KAIST | ICML | 2022 | [PUB] [PDF] |
| Neural Tangent Kernel Empowered Federated Learning | NC State University | ICML | 2022 | [PUB] [PDF] [CODE] |
| Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy | UMN | ICML | 2022 | [PUB] [PDF] |
| Personalized Federated Learning via Variational Bayesian Inference | CAS | ICML | 2022 | [PUB] [PDF] [SLIDE] [UC.] |
| Federated Learning with Label Distribution Skew via Logits Calibration | ZJU | ICML | 2022 | [PUB] |
| Neurotoxin: Durable Backdoors in Federated Learning | Southeast University;Princeton | ICML | 2022 | [PUB] [PDF] [CODE] |
| Resilient and Communication Efficient Learning for Heterogeneous Federated Systems | Michigan State University | ICML | 2022 | [PUB] |
| Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond | KAIST | ICLR (oral) | 2022 | [PUB] [CODE] |
| Bayesian Framework for Gradient Leakage | ETH Zurich | ICLR | 2022 | [PUB] [PDF] [CODE] |
| Federated Learning from only unlabeled data with class-conditional-sharing clients | The University of Tokyo; CUHK | ICLR | 2022 | [PUB] [CODE] |
| FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning | CMU; University of Illinois at Urbana-Champaign; University of Washington | ICLR | 2022 | [PUB] [PDF] |
| Acceleration of Federated Learning with Alleviated Forgetting in Local Training | DO | 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 | Universität von Pennsylvania | ICLR | 2022 | [PUB] [CODE] |
| Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization | Michigan State University; The University of Texas at Austin | ICLR | 2022 | [PUB] [PDF] [CODE] |
| Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models | University of Maryland; NYU | ICLR | 2022 | [PUB] [PDF] [CODE] |
| ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity | University of Cambridge; University of Oxford | ICLR | 2022 | [PUB] [PDF] |
| Diverse Client Selection for Federated Learning via Submodular Maximization | Intel; CMU | ICLR | 2022 | [PUB] [CODE] |
| Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank? | Purdue | ICLR | 2022 | [PUB] [PDF] [CODE] |
| Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions | University of Maryland; Google | ICLR | 2022 | [PUB] [CODE] |
| Towards Model Agnostic Federated Learning Using Knowledge Distillation | EPFL | ICLR | 2022 | [PUB] [PDF] [CODE] |
| Divergence-aware Federated Self-Supervised Learning | NTU; SenseTime | ICLR | 2022 | [PUB] [PDF] [CODE] |
| What Do We Mean by Generalization in Federated Learning? | Stanford; Google | ICLR | 2022 | [PUB] [PDF] [CODE] |
| FedBABU: Toward Enhanced Representation for Federated Image Classification | KAIST | ICLR | 2022 | [PUB] [PDF] [CODE] |
| Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing | EPFL | ICLR | 2022 | [PUB] [PDF] [CODE] |
| Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters | Aibee | ICLR Spotlight | 2022 | [PUB] [PDF] [PAGE] [解读] |
| Hybrid Local SGD for Federated Learning with Heterogeneous Communications | University of Texas; Pennsylvania State University | ICLR | 2022 | [PUB] |
| On Bridging Generic and Personalized Federated Learning for Image Classification | The Ohio State University | ICLR | 2022 | [PUB] [PDF] [CODE] |
| Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond | KAIST; MIT | ICLR | 2022 | [PUB] [PDF] |
| One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them. | JMLR | 2021 | [PUB] [CODE] | |
| Constrained differentially private federated learning for low-bandwidth devices | UAI | 2021 | [PUB] [PDF] | |
| Federated stochastic gradient Langevin dynamics | UAI | 2021 | [PUB] [PDF] | |
| Federated Learning Based on Dynamic Regularization | BU; ARM | ICLR | 2021 | [PUB] [PDF] [CODE] |
| Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning | The Ohio State University | ICLR | 2021 | [PUB] [PDF] |
| HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients | Duke University | ICLR | 2021 | [PUB] [PDF] [CODE] |
| FedMix: Approximation of Mixup under Mean Augmented Federated Learning | KAIST | ICLR | 2021 | [PUB] [PDF] |
| Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms | CMU; Google | ICLR | 2021 | [PUB] [PDF] [CODE] |
| Adaptive Federated Optimization | ICLR | 2021 | [PUB] [PDF] [CODE] | |
| Personalized Federated Learning with First Order Model Optimization | Stanford; NVIDIA | ICLR | 2021 | [PUB] [PDF] [CODE] [UC.] |
| FedBN: Federated Learning on Non-IID Features via Local Batch Normalization | Princeton | ICLR | 2021 | [PUB] [PDF] [CODE] |
| FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning | The Ohio State University | ICLR | 2021 | [PUB] [PDF] [CODE] |
| Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning | KAIST | ICLR | 2021 | [PUB] [PDF] [CODE] |
| KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation | ZJU | ICML | 2021 | [PUB] [PDF] [CODE] [解读] |
| Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix | Harvard University | ICML | 2021 | [PUB] [PDF] [VIDEO] [CODE] |
| FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis | PKU; Princeton | ICML | 2021 | [PUB] [PDF] [VIDEO] |
| Personalized Federated Learning using Hypernetworks | Bar-Ilan University; NVIDIA | ICML | 2021 | [PUB] [PDF] [CODE] [PAGE] [VIDEO] [解读] |
| Federated Composite Optimization | Stanford; Google | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] [SLIDE] |
| Exploiting Shared Representations for Personalized Federated Learning | University of Texas at Austin; Universität von 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; Arm | ICML | 2021 | [PUB] [CODE] [VIDEO] |
| One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning | Toyota; Berkeley; Cornell-Universität | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] |
| CRFL: Certifiably Robust Federated Learning against Backdoor Attacks | UIUC; IBM | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] |
| Federated Learning under Arbitrary Communication Patterns | Indiana University; Amazonas | ICML | 2021 | [PUB] [VIDEO] |
| CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression | CMU | NeurIPS | 2021 | [PUB] [PDF] |
| Boosting with Multiple Sources | NeurIPS | 2021 | [PUB] | |
| DRIVE: One-bit Distributed Mean Estimation | VMware | NeurIPS | 2021 | [PUB] [CODE] |
| Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning | NUS | NeurIPS | 2021 | [PUB] [CODE] |
| Gradient Inversion with Generative Image Prior | POSTECH | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Distributed Machine Learning with Sparse Heterogeneous Data | University of Oxford | NeurIPS | 2021 | [PUB] [PDF] |
| Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning | UCLA | NeurIPS | 2021 | [PUB] [PDF] |
| Sageflow: Robust Federated Learning against Both Stragglers and Adversaries | KAIST | NeurIPS | 2021 | [PUB] |
| CAFE: Catastrophic Data Leakage in Vertical Federated Learning | Rensselaer Polytechnic Institute; IBM Research | NeurIPS | 2021 | [PUB] [CODE] |
| Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee | NUS | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Optimality and Stability in Federated Learning: A Game-theoretic Approach | Cornell-Universität | 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 | THU; 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 | Universität von 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 | THU; 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; Amazonas; Google | NeurIPS | 2021 | [PUB] [PAGE] [SLIDE] |
| FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization | University of North Carolina at Chapel Hill; IBM Research | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Federated Adversarial Domain Adaptation | BU; Columbia University; Rutgers University | ICLR | 2020 | [PUB] [PDF] [CODE] |
| DBA: Distributed Backdoor Attacks against Federated Learning | ZJU; IBM Research | ICLR | 2020 | [PUB] [CODE] |
| Fair Resource Allocation in Federated Learning | CMU; Facebook AI | ICLR | 2020 | [PUB] [PDF] [CODE] |
| Federated Learning with Matched Averaging | University of Wisconsin-Madison; IBM Research | ICLR | 2020 | [PUB] [PDF] [CODE] |
| Differentially Private Meta-Learning | CMU | ICLR | 2020 | [PUB] [PDF] |
| Generative Models for Effective ML on Private, Decentralized Datasets | ICLR | 2020 | [PUB] [PDF] [CODE] | |
| On the Convergence of FedAvg on Non-IID Data | PKU | ICLR | 2020 | [PUB] [PDF] [CODE] [解读] |
| FedBoost: A Communication-Efficient Algorithm for Federated Learning | ICML | 2020 | [PUB] [VIDEO] | |
| FetchSGD: Communication-Efficient Federated Learning with Sketching | UC Berkeley; Johns Hopkins University; Amazonas | ICML | 2020 | [PUB] [PDF] [VIDEO] [CODE] |
| SCAFFOLD: Stochastic Controlled Averaging for Federated Learning | EPFL; Google | ICML | 2020 | [PUB] [PDF] [VIDEO] [UC.] [解读] |
| Federated Learning with Only Positive Labels | ICML | 2020 | [PUB] [PDF] [VIDEO] | |
| From Local SGD to Local Fixed-Point Methods for Federated Learning | Moscow Institute of Physics and Technology; KAUST | ICML | 2020 | [PUB] [PDF] [SLIDE] [VIDEO] |
| Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization | KAUST | ICML | 2020 | [PUB] [PDF] [SLIDE] [VIDEO] |
| Differentially-Private Federated Linear Bandits | MIT | NeurIPS | 2020 | [PUB] [PDF] [CODE] |
| Federated Principal Component Analysis | University of Cambridge; Quine Technologies | NeurIPS | 2020 | [PUB] [PDF] [CODE] |
| FedSplit: an algorithmic framework for fast federated optimization | 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).
| Titel | Zugehörigkeit | Veranstaltungsort | Jahr | Materialien |
|---|---|---|---|---|
| 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 | Universität Cambridge | KDD | 2023 | [PUB] [PDF] |
| FedDefender: Client-Side Attack-Tolerant Federated Learning | KAIST | KDD | 2023 | [PUB] [PDF] [CODE] |
| FedAPEN: Personalized Cross-silo Federated Learning with Adaptability to Statistical Heterogeneity | ZJU | KDD | 2023 | [PUB] [CODE] |
| FedPseudo: Privacy-Preserving Pseudo Value-Based Deep Learning Models for Federated Survival Analysis | UMBC | KDD | 2023 | [PUB] [PDF] |
| ShapleyFL: Robust Federated Learning Based on Shapley Value | ZJU | KDD | 2023 | [PUB] [CODE] |
| Federated Few-shot Learning | University of Virginia | KDD | 2023 | [PUB] [PDF] [CODE] |
| Theoretical Convergence Guaranteed Resource-Adaptive Federated Learning with Mixed Heterogeneity | SDU | KDD | 2023 | [PUB] |
| Personalized Federated Learning with Parameter Propagation | UIUC | KDD | 2023 | [PUB] |
| Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining | University of Pittsburgh | KDD | 2023 | [PUB] [PDF] [CODE] |
| CriticalFL: A Critical Learning Periods Augmented Client Selection Framework for Efficient Federated Learning | SUNY-Binghamton University | KDD | 2023 | [PUB] [PDF] |
| FLAMES2Graph: An Interpretable Federated Multivariate Time Series Classification Framework | L3S Research Center | KDD | 2023 | [PUB] [PDF] |
| FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy | SJTU | KDD | 2023 | [PUB] [PDF] [CODE] |
| Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework | UCSD | KDD | 2023 | [PUB] [PDF] [CODE] |
| DM-PFL: Hitchhiking Generic Federated Learning for Efficient Shift-Robust Personalization | BUAA | KDD | 2023 | [PUB] [CODE] |
| FS-REAL: Towards Real-World Cross-Device Federated Learning | Alibaba Group | 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; Alibaba Group | 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 | DO | KDD | 2022 | [PUB] [PDF] [CODE] |
| Connected Low-Loss Subspace Learning for a Personalization in Federated Learning | Ulsan National Institute of Science and Technology | KDD | 2022 | [PUB] [PDF] [CODE] |
| FedMSplit: Correlation-Adaptive Federated Multi-Task Learning across Multimodal Split Networks | University of Virginia | KDD | 2022 | [PUB] |
| Communication-Efficient Robust Federated Learning with Noisy Labels | University of Pittsburgh | KDD | 2022 | [PUB] [PDF] |
| FLDetector: Detecting Malicious Clients in Federated Learning via Checking Model-Updates Consistency | USTC | KDD | 2022 | [PUB] [PDF] [CODE] |
| Practical Lossless Federated Singular Vector Decomposition Over Billion-Scale Data | HKUST | KDD | 2022 | [PUB] [PDF] [CODE] |
| FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy | SJTU | KDD | 2022 | [PUB] [PDF] |
| FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Platform for Federated Graph Learning | Alibaba | KDD (Best Paper Award) | 2022 | [PUB] [PDF] [CODE] |
| Fed-LTD: Towards Cross-Platform Ride Hailing via Federated Learning to Dispatch | BUAA | KDD | 2022 | [PUB] [PDF] [解读] |
| Felicitas: Federated Learning in Distributed Cross Device Collaborative Frameworks | USTC | KDD | 2022 | [PUB] [PDF] |
| No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices | Renmin University of China | KDD | 2022 | [PUB] [PDF] |
| FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling | DO | KDD | 2022 | [PUB] [PDF] [CODE] |
| PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion | The University of Queensland | WSDM | 2022 | [PUB] [PDF] |
| Fed2: Feature-Aligned Federated Learning | George Mason University; Microsoft; University of Maryland | KDD | 2021 | [PUB] [PDF] |
| FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data | Nanjing University | KDD | 2021 | [PUB] [CODE] |
| Federated Adversarial Debiasing for Fair and Trasnferable Representations | Michigan State University | KDD | 2021 | [PUB] [PAGE] [CODE] [SLIDE] |
| Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling | USC | KDD | 2021 | [PUB] [CODE] [解读] |
| AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization | Xidian University;JD Tech | KDD | 2021 | [PUB] [PDF] |
| FLOP: Federated Learning on Medical Datasets using Partial Networks | Duke University | KDD | 2021 | [PUB] [PDF] [CODE] |
| A Practical Federated Learning Framework for Small Number of Stakeholders | ETH Zürich | WSDM | 2021 | [PUB] [CODE] |
| Federated Deep Knowledge Tracing | USTC | WSDM | 2021 | [PUB] [CODE] |
| FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems | University College Dublin | KDD | 2020 | [PUB] [VIDEO] |
| Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data | JD Tech | KDD | 2020 | [PUB] [PDF] [VIDEO] |
| Federated Online Learning to Rank with Evolution Strategies | Facebook AI Research | WSDM | 2019 | [PUB] [CODE] |
Federated Learning papers accepted by top Secure conference and journal, Including S&P(IEEE Symposium on Security and Privacy), CCS(Conference on Computer and Communications Security), USENIX Security(Usenix Security Symposium) and NDSS(Network and Distributed System Security Symposium).
| Titel | Zugehörigkeit | Veranstaltungsort | Jahr | Materialien |
|---|---|---|---|---|
| Byzantine-Robust Decentralized Federated Learning | CCS | 2024 | [PUB] | |
| Not One Less: Exploring Interplay between User Profiles and Items in Untargeted Attacks against Federated Recommendation | CCS | 2024 | [PUB] | |
| Cross-silo Federated Learning with Record-level Personalized Differential Privacy. | CCS | 2024 | [PUB] | |
| Samplable Anonymous Aggregation for Private Federated Data Analysis | CCS | 2024 | [PUB] | |
| Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential Privacy | CCS | 2024 | [PUB] | |
| Distributed Backdoor Attacks on Federated Graph Learning and Certified Defenses | CCS | 2024 | [PUB] | |
| Two-Tier Data Packing in RLWE-based Homomorphic Encryption for Secure Federated Learning. | CCS | 2024 | [PUB] | |
| Poster: Protection against Source Inference Attacks in Federated Learning using Unary Encoding and Shuffling. | CCS | 2024 | [PUB] | |
| Poster: End-to-End Privacy-Preserving Vertical Federated Learning using Private Cross-Organizational Data Collaboration. | CCS | 2024 | [PUB] | |
| FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting | NDSS | 2024 | [PUB] | |
| FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning | NDSS | 2024 | [PUB] | |
| Automatic Adversarial Adaption for Stealthy Poisoning Attacks in Federated Learning | NDSS | 2024 | [PUB] | |
| CrowdGuard: Federated Backdoor Detection in Federated Learning | NDSS | 2024 | [PUB] | |
| Protecting Label Distribution in Cross-Silo Federated Learning | S&P | 2024 | [PUB] | |
| FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks | S&P | 2024 | [PUB] | |
| BadVFL: Backdoor Attacks in Vertical Federated Learning | S&P | 2024 | [PUB] | |
| SHERPA: Explainable Robust Algorithms for Privacy-Preserved Federated Learning in Future Networks to Defend Against Data Poisoning Attacks | S&P | 2024 | [PUB] | |
| Loki: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation | S&P | 2024 | [PUB] | |
| Poster: Towards Privacy-Preserving Federated Recommendation via Synthetic Interactions. | S&P Workshop | 2024 | [PUB] | |
| A Performance Analysis for Confidential Federated Learning. | S&P Workshop | 2024 | [PUB] | |
| Turning Privacy-preserving Mechanisms against Federated Learning | University of Pavia | CCS | 2023 | [PUB] [PDF] |
| MESAS: Poisoning Defense for Federated Learning Resilient against Adaptive Attackers | University of Würzburg | CCS | 2023 | [PUB] |
| martFL: Enabling Utility-Driven Data Marketplace with a Robust and Verifiable Federated Learning Architecture | DO | CCS | 2023 | [PUB] [PDF] [CODE] |
| Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks | UIUC | CCS | 2023 | [PUB] [PDF] |
| Poster: Verifiable Data Valuation with Strong Fairness in Horizontal Federated Learning | NSYSU | CCS | 2023 | [PUB] |
| Poster: Bridging Trust Gaps: Data Usage Transparency in Federated Data Ecosystems | RWTH Aachen University | CCS | 2023 | [PUB] |
| Every Vote Counts: Ranking-Based Training of Federated Learning to Resist Poisoning Attacks | University of Massachusetts Amherst | USENIX Security | 2023 | [PUB] [PDF] |
| PrivateFL: Accurate, Differentially Private Federated Learning via Personalized Data Transformation | JHU | USENIX Security | 2023 | [PUB] [CODE] |
| Gradient Obfuscation Gives a False Sense of Security in Federated Learning | NCSU | USENIX Security | 2023 | [PUB] [PDF] [CODE] |
| FedVal: Different good or different bad in federated learning | AI Sweden | USENIX Security | 2023 | [PUB] [PDF] [CODE] |
| Securing Federated Sensitive Topic Classification against Poisoning Attacks | IMDEA Networks Institute | NDSS | 2023 | [PUB] [PDF] [CODE] |
| PPA: Preference Profiling Attack Against Federated Learning | NJUST | NDSS | 2023 | [PUB] [PDF] |
| Turning Privacy-preserving Mechanisms against Federated Learning | University of Pavia; TU Delft; University of Padua; Radboud University | CCS | 2023 | [PUB] [PDF] [CODE] |
| CERBERUS: Exploring Federated Prediction of Security Events | UCL London | CCS | 2022 | [PUB] [PDF] |
| EIFFeL: Ensuring Integrity for Federated Learning | UW-Madison | CCS | 2022 | [PUB] [PDF] |
| Eluding Secure Aggregation in Federated Learning via Model Inconsistency | SPRING Lab; EPFL | CCS | 2022 | [PUB] [PDF] [CODE] |
| Federated Boosted Decision Trees with Differential Privacy | University of Warwick | CCS | 2022 | [PUB] [PDF] [CODE] |
| FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information | Duke University | S&P | 2023 | [PUB] [PDF] |
| Scalable and Privacy-Preserving Federated Principal Component Analysis | EPFL; Tune Insight SA | S&P | 2023 | [PUB] [PDF] |
| SafeFL: MPC-friendly Framework for Private and Robust Federated Learning | TU Darmstadt | S&P Workshop | 2023 | [PUB] |
| On the Pitfalls of Security Evaluation of Robust Federated Learning. | umass | S&P Workshop | 2023 | [PUB] |
| BayBFed: Bayesian Backdoor Defense for Federated Learning | TU Darmstadt; UTSA | S&P | 2023 | [PUB] [PDF] |
| 3DFed: Adaptive and Extensible Framework for Covert Backdoor Attack in Federated Learning | PolyU | S&P | 2023 | [PUB] [CODE] |
| RoFL: Robustness of Secure Federated Learning. | ETH Zurich | S&P | 2023 | [PUB] [PDF] [CODE] |
| Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning. | upenn | S&P | 2023 | [PUB] [CODE] |
| ELSA: Secure Aggregation for Federated Learning with Malicious Actors. | S&P | 2023 | ||
| Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy | Fudan University | S&P | 2023 | [PUB] [PDF] |
| Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning | University of Massachusetts | S&P | 2022 | [PUB] [VIDEO] |
| SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost | Microsoft Research | USENIX Security | 2022 | [PUB] [PDF] [CODE] [VIDEO] [SUPP] |
| Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors | University of Vermont | USENIX Security | 2022 | [PUB] [SLIDE] [VIDEO] |
| Label Inference Attacks Against Vertical Federated Learning | ZJU | USENIX Security | 2022 | [PUB] [SLIDE] [CODE] [VIDEO] |
| FLAME: Taming Backdoors in Federated Learning | Technical University of Darmstadt | USENIX Security | 2022 | [PUB] [SLIDE] [PDF] [VIDEO] |
| Local and Central Differential Privacy for Robustness and Privacy in Federated Learning | University at Buffalo, SUNY | NDSS | 2022 | [PUB] [PDF] [VIDEO] [UC.] |
| Interpretable Federated Transformer Log Learning for Cloud Threat Forensics | University of the Incarnate Word | NDSS | 2022 | [PUB] [VIDEO] [UC.] |
| FedCRI: Federated Mobile Cyber-Risk Intelligence | Technical University of Darmstadt | NDSS | 2022 | [PUB] [VIDEO] |
| DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection | Technical University of Darmstadt | NDSS | 2022 | [PUB] [PDF] [VIDEO] |
| Private Hierarchical Clustering in Federated Networks | NUS | CCS | 2021 | [PUB] [PDF] |
| FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping | Duke University | NDSS | 2021 | [PUB] [PDF] [CODE] [VIDEO] [SLIDE] |
| POSEIDON: Privacy-Preserving Federated Neural Network Learning | EPFL | NDSS | 2021 | [PUB] [VIDEO] |
| Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning | University of Massachusetts Amherst | NDSS | 2021 | [PUB] [CODE] [VIDEO] |
| SAFELearn: Secure Aggregation for private FEderated Learning | TU Darmstadt | S&P Workshop | 2021 | [PUB] |
| Local Model Poisoning Attacks to Byzantine-Robust Federated Learning | The Ohio State University | USENIX Security | 2020 | [PUB] [PDF] [CODE] [VIDEO] [SLIDE] |
| A Reliable and Accountable Privacy-Preserving Federated Learning Framework using the Blockchain | Universität von Kansas | CCS (Poster) | 2019 | [PUB] |
| IOTFLA : A Secured and Privacy-Preserving Smart Home Architecture Implementing Federated Learning | Université du Québéc á Montréal | S&P Workshop | 2019 | [PUB] |
| Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning | University of Massachusetts Amherst | S&P | 2019 | [PUB] [VIDEO] [SLIDE] [CODE] |
| Practical Secure Aggregation for Privacy Preserving Machine Learning | CCS | 2017 | [PUB] [PDF] [解读] [UC.] [UC] |
Federated Learning papers accepted by top CV(computer vision) conference and journal, Including CVPR(Computer Vision and Pattern Recognition), ICCV(IEEE International Conference on Computer Vision), ECCV(European Conference on Computer Vision), MM(ACM International Conference on Multimedia), IJCV(International Journal of Computer Vision).
| Titel | Zugehörigkeit | Veranstaltungsort | Jahr | Materialien |
|---|---|---|---|---|
| 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 | UT | 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 | University of Pittsburgh | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learning | UCF | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning | TCL AI Lab | ICCV | 2023 | [PUB] [PDF] [SUPP] |
| FedPD: Federated Open Set Recognition with Parameter Disentanglement | City University of Hong Kong | ICCV | 2023 | [PUB] [CODE] |
| TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation | ETH Zurich; Sony AI | ICCV | 2023 | [PUB] [PDF] [CODE] |
| Towards Instance-adaptive Inference for Federated Learning | A*STAR | ICCV | 2023 | [PUB] [PDF] [CODE] |
| Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence | SCU; Engineering Research Center of Machine Learning and Industry Intelligence | ICCV | 2023 | [PUB] [PDF] [CODE] |
| zPROBE: Zero Peek Robustness Checks for Federated Learning | Purdue University | ICCV | 2023 | [PUB] [PDF] [SUPP] |
| ProtoFL: Unsupervised Federated Learning via Prototypical Distillation | KakaoBank Corp. | ICCV | 2023 | [PUB] [PDF] |
| MAS: Towards Resource-Efficient Federated Multiple-Task Learning | Sony AI | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation | PKU | ICCV | 2023 | [PUB] [PDF] [SUPP] |
| When Do Curricula Work in Federated Learning? | UCSD | ICCV | 2023 | [PUB] [PDF] [SUPP] |
| Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples | Duke University | ICCV | 2023 | [PUB] [PDF] [CODE] |
| Multi-Metrics Adaptively Identifies Backdoors in Federated Learning | SCUT | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier | ZJU | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation | Ludwig Maximilian University of Munich; Siemens Technology | ICCV | 2023 | [PUB] [PDF] [SUPP] |
| Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration | BUAA | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| Global Balanced Experts for Federated Long-Tailed Learning | CUHK-Shenzhen | ICCV | 2023 | [PUB] [CODE] [SUPP] |
| Knowledge-Aware Federated Active Learning with Non-IID Data | 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-Universität | 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 | ÄH | 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 | DO | CVPR | 2023 | [PUB] [PDF] [CODE] |
| Confidence-Aware Personalized Federated Learning via Variational Expectation Maximization | KU Leuven | CVPR | 2023 | [PUB] [PDF] [CODE] |
| STDLens: Model Hijacking-Resilient Federated Learning for Object Detection | GaTech | CVPR | 2023 | [PUB] [PDF] [CODE] |
| Re-Thinking Federated Active Learning Based on Inter-Class Diversity | KAIST | CVPR | 2023 | [PUB] [PDF] [CODE] |
| Learning Federated Visual Prompt in Null Space for MRI Reconstruction | A*STAR | CVPR | 2023 | [PUB] [PDF] [CODE] |
| Fair Federated Medical Image Segmentation via Client Contribution Estimation | CUHK | CVPR | 2023 | [PUB] [PDF] [CODE] |
| Federated Learning With Data-Agnostic Distribution Fusion | NJU | CVPR | 2023 | [PUB] [CODE] |
| How To Prevent the Poor Performance Clients for Personalized Federated Learning? | CSU | CVPR | 2023 | [PUB] |
| GradMA: A Gradient-Memory-Based Accelerated Federated Learning With Alleviated Catastrophic Forgetting | ECNU | CVPR | 2023 | [PUB] [PDF] [CODE] |
| Bias-Eliminating Augmentation Learning for Debiased Federated Learning | NTU | CVPR | 2023 | [PUB] |
| Federated Incremental Semantic Segmentation | CAS; UCAS | CVPR | 2023 | [PUB] [PDF] [CODE] |
| Asynchronous Federated Continual Learning | University of Padova | CVPR workshop | 2023 | [PUB] [PDF] [SILDES] [CODE] |
| Mixed Quantization Enabled Federated Learning To Tackle Gradient Inversion Attacks | UMBC | CVPR workshop | 2023 | [PUB] [CODE] |
| OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework | Meituan | CVPR workshop | 2023 | [PUB] [PDF] [CODE] |
| Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Data | utexas | CVPR workshop | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| TimelyFL: Heterogeneity-Aware Asynchronous Federated Learning With Adaptive Partial Training | USC | CVPR workshop | 2023 | [PUB] [PDF] |
| Many-Task Federated Learning: A New Problem Setting and a Simple Baseline | utexas | CVPR workshop | 2023 | [PUB] [CODE] |
| Confederated Learning: Going Beyond Centralization | CAS; UCAS | 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 | SCHLAG | CVPR | 2022 | [PUB] |
| Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning | Stanford | CVPR | 2022 | [PUB] [SUPP] [PDF] [CODE] [VIDEO] |
| FedCorr: Multi-Stage Federated Learning for Label Noise Correction | Singapore University of Technology and Design | CVPR | 2022 | [PUB] [SUPP] [PDF] [CODE] [VIDEO] |
| FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning | Duke University | CVPR | 2022 | [PUB] [SUPP] [PDF] |
| Layer-Wised Model Aggregation for Personalized Federated Learning | PolyU | CVPR | 2022 | [PUB] [SUPP] [PDF] |
| Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning | University of Central Florida | CVPR | 2022 | [PUB] [SUPP] [PDF] [CODE] |
| Federated Learning With Position-Aware Neurons | Nanjing University | CVPR | 2022 | [PUB] [SUPP] [PDF] |
| RSCFed: Random Sampling Consensus Federated Semi-Supervised Learning | HKUST | CVPR | 2022 | [PUB] [SUPP] [PDF] [CODE] |
| Learn From Others and Be Yourself in Heterogeneous Federated Learning | Wuhan University | CVPR | 2022 | [PUB] [CODE] [VIDEO] |
| Robust Federated Learning With Noisy and Heterogeneous Clients | Wuhan University | CVPR | 2022 | [PUB] [SUPP] [CODE] |
| ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning | Arizona State University | CVPR | 2022 | [PUB] [SUPP] [PDF] [CODE] |
| FedDC: Federated Learning With Non-IID Data via Local Drift Decoupling and Correction | National University of Defense Technology | CVPR | 2022 | [PUB] [PDF] [CODE] [解读] |
| Federated Class-Incremental Learning | CAS; Northwestern University; UTS | CVPR | 2022 | [PUB] [PDF] [CODE] |
| Fine-Tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning | PKU; JD Explore Academy; 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 | Univ. of Pittsburgh; NVIDIA | CVPR | 2022 | [PUB] [PDF] |
| Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning | HHI | CVPR workshop | 2022 | [PUB] [PDF] [SILDES] [VIDEO] |
| MPAF: Model Poisoning Attacks to Federated Learning Based on Fake Clients | Duke University | CVPR workshop | 2022 | [PUB] [PDF] [SILDES] [VIDEO] |
| Communication-Efficient Federated Data Augmentation on Non-IID Data | UESTC | CVPR workshop | 2022 | [PUB] |
| Does Federated Dropout Actually Work? | Stanford | CVPR workshop | 2022 | [PUB] [VIDEO] |
| FedIris: Towards More Accurate and Privacy-preserving Iris Recognition via Federated Template Communication | USTC; CRIPAC; CASIA | CVPR workshop | 2022 | [PUB] [SLIDES] [VIDEO] |
| Multi-Institutional Collaborations for Improving Deep Learning-Based Magnetic Resonance Image Reconstruction Using Federated Learning | Johns Hopkins University | CVPR | 2021 | [PUB] [PDF] [CODE] |
| Model-Contrastive Federated Learning | NUS; UC Berkeley | CVPR | 2021 | [PUB] [PDF] [CODE] [解读] |
| FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space | CUHK | CVPR | 2021 | [PUB] [PDF] [CODE] |
| Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective | Duke University | CVPR | 2021 | [PUB] [PDF] [CODE] |
| Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment | PKU | ICCV | 2021 | [PUB] |
| Ensemble Attention Distillation for Privacy-Preserving Federated Learning | University at Buffalo | ICCV | 2021 | [PUB] [PDF] |
| Collaborative Unsupervised Visual Representation Learning from Decentralized Data | NTU; SenseTime | ICCV | 2021 | [PUB] [PDF] |
| Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identification | NTU | 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).
| Titel | Zugehörigkeit | Veranstaltungsort | Jahr | Materialien |
|---|---|---|---|---|
| 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 | Auburn-Universität | EMNLP | 2023 | [PUB] [PDF] [CODE] |
| Federated Meta-Learning for Emotion and Sentiment Aware Multi-modal Complaint Identification | IIT Patna | EMNLP | 2023 | [PUB] [CODE] |
| FedID: Federated Interactive Distillation for Large-Scale Pretraining Language Models | YNU | EMNLP | 2023 | [PUB] [CODE] |
| FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning | KAIST | EMNLP | 2023 | [PUB] [PDF] |
| Coordinated Replay Sample Selection for Continual Federated Learning | CMU | EMNLP industry Track | 2023 | [PUB] [PDF] |
| Tunable Soft Prompts are Messengers in Federated Learning | SYSU | EMNLP Findings | 2023 | [PUB] [PDF] [CODE] |
| Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms | OSU | ACL | 2023 | [PUB] [PDF] [CODE] |
| FEDLEGAL: The First Real-World Federated Learning Benchmark for Legal NLP | SCHLAG; Peng Cheng Lab | ACL | 2023 | [PUB] [CODE] |
| Client-Customized Adaptation for Parameter-Efficient Federated Learning | ACL Findings | 2023 | [PUB] | |
| Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter | ACL Findings | 2023 | [PUB] [PDF] [CODE] | |
| Federated Domain Adaptation for Named Entity Recognition via Distilling with Heterogeneous Tag Sets | ACL Findings | 2023 | [PUB] | |
| FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre-trained Language Models | ACL Findings | 2023 | [PUB] | |
| Federated Learning of Gboard Language Models with Differential Privacy | ACL Industry Track | 2023 | [PUB] [PDF] | |
| Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling | SNU | EMNLP | 2022 | [PUB] [PDF] |
| A Federated Approach to Predicting Emojis in Hindi Tweets | University of Alberta | EMNLP | 2022 | [PUB] [PDF] [CODE] |
| Federated Model Decomposition with Private Vocabulary for Text Classification | SCHLAG; Peng Cheng Lab | EMNLP | 2022 | [PUB] [CODE] |
| Fair NLP Models with Differentially Private Text Encoders | Univ. Lille | EMNLP | 2022 | [PUB] [PDF] [CODE] |
| Federated Continual Learning for Text Classification via Selective Inter-client Transfer | DRIMCo GmbH; LMU | EMNLP Findings | 2022 | [PUB] [PDF] [CODE] |
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation kg. | Lehigh University | EMNLP Findings | 2022 | [PUB] [PDF] [CODE] |
| Dim-Krum: Backdoor-Resistant Federated Learning for NLP with Dimension-wise Krum-Based Aggregation | PKU | EMNLP Findings | 2022 | [PUB] [PDF] |
| Scaling Language Model Size in Cross-Device Federated Learning | ACL workshop | 2022 | [PUB] [PDF] | |
| Intrinsic Gradient Compression for Scalable and Efficient Federated Learning | Oxford | ACL workshop | 2022 | [PUB] [PDF] |
| ActPerFL: Active Personalized Federated Learning | Amazonas | ACL workshop | 2022 | [PUB] [PAGE] |
| FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks | USC | NAACL | 2022 | [PUB] [PDF] [CODE] |
| Federated Learning with Noisy User Feedback | USC; Amazonas | NAACL | 2022 | [PUB] [PDF] |
| Training Mixed-Domain Translation Models via Federated Learning | Amazonas | NAACL | 2022 | [PUB] [PAGE] [PDF] |
| Pretrained Models for Multilingual Federated Learning | Johns Hopkins University | 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; Amazonas | NAACL workshop | 2021 | [PUB] [PDF] |
| An Investigation towards Differentially Private Sequence Tagging in a Federated Framework | Universität Hamburg | NAACL workshop | 2021 | [PUB] |
| Understanding Unintended Memorization in Language Models Under Federated Learning | NAACL workshop | 2021 | [PUB] [PDF] | |
| FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction | CAS | EMNLP | 2020 | [PUB] [VIDEO] [解读] |
| Empirical Studies of Institutional Federated Learning For Natural Language Processing | Ping An Technology | EMNLP workshop | 2020 | [PUB] |
| Federated Learning for Spoken Language Understanding | PKU | COLING | 2020 | [PUB] |
| Two-stage Federated Phenotyping and Patient Representation Learning | Boston Children's Hospital Harvard Medical School | ACL workshop | 2019 | [PUB] [PDF] [CODE] [UC.] |
Federated Learning papers accepted by top Information Retrieval conference and journal, including SIGIR(Annual International ACM SIGIR Conference on Research and Development in Information Retrieval).
| Titel | Zugehörigkeit | Veranstaltungsort | Jahr | Materialien |
|---|---|---|---|---|
| ReFer: Retrieval-Enhanced Vertical Federated Recommendation for Full Set User Benefit | DO | 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 | Alibaba Group | 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 | Alibaba Group | 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 | Technische Universität München | 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).
| Titel | Zugehörigkeit | Veranstaltungsort | Jahr | Materialien |
|---|---|---|---|---|
| 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 | Alibaba Group | 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 | BISSCHEN | 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. | BISSCHEN | VLDB | 2023 | [PUB] [CODE] |
| FS-Real: A Real-World Cross-Device Federated Learning Platform. | Alibaba Group | VLDB | 2023 | [PUB] [PDF] [CODE] |
| Federated Calibration and Evaluation of Binary Classifiers. | Meta | VLDB | 2023 | [PUB] [PDF] [CODE] |
| Olive: Oblivious Federated Learning on Trusted Execution Environment Against the Risk of Sparsification. | Kyoto University | VLDB | 2023 | [PUB] [PDF] [CODE] |
| Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System. | NUS | VLDB | 2023 | [PUB] [CODE] |
| Differentially Private Vertical Federated Clustering. | Purdue University | VLDB | 2023 | [PUB] [PDF] [CODE] |
| FederatedScope: A Flexible Federated Learning Platform for Heterogeneity. | Alibaba | VLDB | 2023 | [PUB] [PDF] [CODE] |
| Secure Shapley Value for Cross-Silo Federated Learning. | Kyoto University | VLDB | 2023 | [PUB] [PDF] [CODE] |
| OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization | ZJU | VLDB | 2022 | [PUB] [PDF] [CODE] |
| Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy. | NUS | VLDB | 2022 | [PUB] [CODE] |
| Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Update | PKU | VLDB | 2022 | [PUB] [PDF] [CODE] |
| FedTSC: A Secure Federated Learning System for Interpretable Time Series Classification. | SCHLAG | VLDB | 2022 | [PUB] [CODE] |
| Improving Fairness for Data Valuation in Horizontal Federated Learning | The UBC | ICDE | 2022 | [PUB] [PDF] |
| FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneity | USTC | ICDE | 2022 | [PUB] [PDF] [CODE] |
| FedMP: Federated Learning through Adaptive Model Pruning in Heterogeneous Edge Computing. | USTC | ICDE | 2022 | [PUB] |
| Federated Learning on Non-IID Data Silos: An Experimental Study. | NUS | ICDE | 2022 | [PUB] [PDF] [CODE] |
| Enhancing Federated Learning with Intelligent Model Migration in Heterogeneous Edge Computing | USTC | ICDE | 2022 | [PUB] |
| Samba: A System for Secure Federated Multi-Armed Bandits | Univ. Clermont Auvergne | ICDE | 2022 | [PUB] [CODE] |
| FedRecAttack: Model Poisoning Attack to Federated Recommendation | ZJU | ICDE | 2022 | [PUB] [PDF] [CODE] |
| Enhancing Federated Learning with In-Cloud Unlabeled Data | USTC | ICDE | 2022 | [PUB] |
| Efficient Participant Contribution Evaluation for Horizontal and Vertical Federated Learning | USTC | ICDE | 2022 | [PUB] |
| An Introduction to Federated Computation | University of Warwick; Facebook | SIGMOD Tutorial | 2022 | [PUB] |
| BlindFL: Vertical Federated Machine Learning without Peeking into Your Data | PKU; Tencent | SIGMOD | 2022 | [PUB] [PDF] |
| An Efficient Approach for Cross-Silo Federated Learning to Rank | BUAA | ICDE | 2021 | [PUB] [RELATED PAPER(ZH)] |
| Feature Inference Attack on Model Predictions in Vertical Federated Learning | NUS | ICDE | 2021 | [PUB] [PDF] [CODE] |
| Efficient Federated-Learning Model Debugging | USTC | ICDE | 2021 | [PUB] |
| Federated Matrix Factorization with Privacy Guarantee | Purdue | VLDB | 2021 | [PUB] |
| Projected Federated Averaging with Heterogeneous Differential Privacy. | Renmin University of China | VLDB | 2021 | [PUB] [CODE] |
| Enabling SQL-based Training Data Debugging for Federated Learning | Simon Fraser University | VLDB | 2021 | [PUB] [PDF] [CODE] |
| Refiner: A Reliable Incentive-Driven Federated Learning System Powered by Blockchain | ZJU | VLDB | 2021 | [PUB] |
| Tanium Reveal: A Federated Search Engine for Querying Unstructured File Data on Large Enterprise Networks | Tanium Inc. | VLDB | 2021 | [PUB] [VIDEO] |
| VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning | PKU | SIGMOD | 2021 | [PUB] |
| ExDRa: Exploratory Data Science on Federated Raw Data | SIEMENS | SIGMOD | 2021 | [PUB] |
| Joint blockchain and federated learning-based offloading in harsh edge computing environments | TJU | SIGMOD workshop | 2021 | [PUB] |
| Privacy Preserving Vertical Federated Learning for Tree-based Models | NUS | VLDB | 2020 | [PUB] [PDF] [VIDEO] [CODE] |
Federated Learning papers accepted by top Database conference and journal, including SIGCOMM(Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication), INFOCOM(IEEE Conference on Computer Communications), MobiCom(ACM/IEEE International Conference on Mobile Computing and Networking), NSDI(Symposium on Networked Systems Design and Implementation) and WWW(The Web Conference).
| Titel | Zugehörigkeit | Veranstaltungsort | Jahr | Materialien |
|---|---|---|---|---|
| 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 | ICH S | WWW (Companion Volume) | 2024 | [PUB] |
| Phoenix: A Federated Generative Diffusion Model | UW | WWW (Companion Volume) | 2024 | [PUB] |
| Federated Learning in Large Model Era: Vision-Language Model for Smart City Safety Operation Management | ENN; UPC | WWW (Companion Volume) | 2024 | [PUB] |
| Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks | USTC | WWW (Companion Volume) | 2024 | [PUB] [PDF] |
| GradFilt: Class-wise Targeted Data Reconstruction from Gradients in Federated Learning | PolyU | WWW (Companion Volume) | 2024 | [PUB] |
| Detecting Poisoning Attacks on Federated Learning Using Gradient-Weighted Class Activation Mapping | ISEP | WWW (Companion Volume) | 2024 | [PUB] |
| AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving | NTU | MobiCom | 2023 | [PUB] [PDF] |
| Efficient Federated Learning for Modern NLP | Beiyou Shenzhen Institute | MobiCom | 2023 | [PDF] [解读] |
| FLASH: Towards a High-performance Hardware Acceleration Architecture for Cross-silo Federated Learning | HKUST; Clustar | NSDI | 2023 | [PUB] [SLIDE] [VIDEO] |
| To Store or Not? Online Data Selection for Federated Learning with Limited Storage. | SJTU | WWW | 2023 | [PUB] [PDF] |
| pFedPrompt: Learning Personalized Prompt for Vision-Language Models in Federated Learning. | PolyU | WWW | 2023 | [PUB] |
| Quantifying and Defending against Privacy Threats on Federated Knowledge Graph Embedding. | ZJU; HIC-ZJU | WWW | 2023 | [PUB] [PDF] |
| Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks | PKU | WWW | 2023 | [PUB] [PDF] [CODE] |
| Semi-decentralized Federated Ego Graph Learning for Recommendation | SUST | WWW | 2023 | [PUB] [PDF] |
| FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures. | Swinburne | WWW | 2023 | [PUB] [CODE] |
| FedEdge: Accelerating Edge-Assisted Federated Learning. | Swinburne | WWW | 2023 | [PUB] |
| Federated Node Classification over Graphs with Latent Link-type Heterogeneity. | 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 Leuven | WWW (Companion Volume) | 2023 | [PUB] |
| Privacy-Preserving Online Content Moderation: A Federated Learning Use Case. | SCHNEIDEN | WWW (Companion Volume) | 2023 | [PUB] [PDF] |
| Privacy-Preserving Online Content Moderation with Federated Learning. | SCHNEIDEN | WWW (Companion Volume) | 2023 | [PUB] |
| A Federated Learning Benchmark for Drug-Target Interaction. | University of Turin | WWW (Companion Volume) | 2023 | [PUB] [PDF] [CODE] |
| Towards a Decentralized Data Hub and Query System for Federated Dynamic Data Spaces. | TU Berlin | WWW (Companion Volume) | 2023 | [PUB] |
| 1st Workshop on Federated Learning Technologies1st Workshop on Federated Learning Technologies | University of Turin | WWW (Companion Volume) | 2023 | [PUB] |
| A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness and Privacy | CUHK | WWW (Companion Volume) | 2023 | [PUB] [PDF] |
| A Hierarchical Knowledge Transfer Framework for Heterogeneous Federated Learning | DO | INFOCOM | 2023 | [PUB] |
| A Reinforcement Learning Approach for Minimizing Job Completion Time in Clustered Federated Learning | Southeast University | INFOCOM | 2023 | [PUB] |
| Adaptive Configuration for Heterogeneous Participants in Decentralized Federated Learning | USTC | INFOCOM | 2023 | [PUB] [PDF] |
| AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices | Guangdong University of Technology | INFOCOM | 2023 | [PUB] [PDF] |
| AOCC-FL: Federated Learning with Aligned Overlapping via Calibrated Compensation | HUST | INFOCOM | 2023 | [PUB] |
| Asynchronous Federated Unlearning | Universität von 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 | Northwestern University | INFOCOM | 2023 | [PUB] |
| More than Enough is Too Much: Adaptive Defenses against Gradient Leakage in Production Federated Learning | Universität von Toronto | INFOCOM | 2023 | [PUB] [PDF] [WEIBO] |
| Network Adaptive Federated Learning: Congestion and Lossy Compression | UTAustin | INFOCOM | 2023 | [PUB] [PDF] |
| OBLIVION: Poisoning Federated Learning by Inducing Catastrophic Forgetting | The Hang Seng University of Hong Kong | INFOCOM | 2023 | [PUB] [CODE] |
| Privacy as a Resource in Differentially Private Federated Learning | BUPT | INFOCOM | 2023 | [PUB] |
| SplitGP: Achieving Both Generalization and Personalization in Federated Learning | KAIST | INFOCOM | 2023 | [PUB] [PDF] |
| SVDFed: Enabling Communication-Efficient Federated Learning via Singular-Value-Decomposition | Beihang University | INFOCOM | 2023 | [PUB] |
| Tackling System Induced Bias in Federated Learning: Stratification and Convergence Analysis | Southern University of Science and Technology | INFOCOM | 2023 | [PUB] [PDF] |
| Toward Sustainable AI: Federated Learning Demand Response in Cloud-Edge Systems via Auctions | BUPT | INFOCOM | 2023 | [PUB] [PDF] |
| Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labeling | Auburn-Universität | 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 | Universität von Toronto | INFOCOM | 2022 | [PUB] |
| Optimal Rate Adaption in Federated Learning with Compressed Communications | SZU | INFOCOM | 2022 | [PUB] [PDF] |
| The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining. | CityU | INFOCOM | 2022 | [PUB] [PDF] |
| Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling. | CUHK; AIRS ;Yale University | INFOCOM | 2022 | [PUB] [PDF] |
| Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization | Army Research Laboratory, Adelphi | INFOCOM | 2022 | [PUB] [PDF] |
| FLASH: Federated Learning for Automated Selection of High-band mmWave Sectors | NEU | INFOCOM | 2022 | [PUB] [CODE] |
| A Profit-Maximizing Model Marketplace with Differentially Private Federated Learning | CUHK; AIRS | INFOCOM | 2022 | [PUB] |
| Protect Privacy from Gradient Leakage Attack in Federated Learning | PolyU | INFOCOM | 2022 | [PUB] [SLIDE] |
| FedFPM: A Unified Federated Analytics Framework for Collaborative Frequent Pattern Mining. | SJTU | INFOCOM | 2022 | [PUB] [CODE] |
| An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning | SWJTU;THU | WWW | 2022 | [PUB] [PDF] [CODE] |
| LocFedMix-SL: Localize, Federate, and Mix for Improved Scalability, Convergence, and Latency in Split Learning | Yonsei University | WWW | 2022 | [PUB] |
| Federated Unlearning via Class-Discriminative Pruning | PolyU | WWW | 2022 | [PUB] [PDF] [CODE] |
| FedKC: Federated Knowledge Composition for Multilingual Natural Language Understanding | Purdue | WWW | 2022 | [PUB] |
| Powering Multi-Task Federated Learning with Competitive GPU Resource Sharing. | WWW (Companion Volume) | 2022 | ||
| Federated Bandit: A Gossiping Approach | University of California | SIGMETRICS | 2021 | [PUB] [PDF] |
| Hermes: an efficient federated learning framework for heterogeneous mobile clients | Duke University | MobiCom | 2021 | [PUB] |
| Federated mobile sensing for activity recognition | Samsung AI Center | MobiCom | 2021 | [PUB] [PAGE] [TALKS] [VIDEO] |
| Learning for Learning: Predictive Online Control of Federated Learning with Edge Provisioning. | Nanjing University | INFOCOM | 2021 | [PUB] |
| Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation. | Purdue | INFOCOM | 2021 | [PUB] [PDF] |
| FAIR: Quality-Aware Federated Learning with Precise User Incentive and Model Aggregation | DO | 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-Universität | INFOCOM | 2021 | [PUB] [PDF] |
| An Incentive Mechanism for Cross-Silo Federated Learning: A Public Goods Perspective | The UBC | INFOCOM | 2021 | [PUB] |
| Resource-Efficient Federated Learning with Hierarchical Aggregation in Edge Computing | USTC | INFOCOM | 2021 | [PUB] |
| FedServing: A Federated Prediction Serving Framework Based on Incentive Mechanism. | Jinan University; CityU | INFOCOM | 2021 | [PUB] [PDF] |
| Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach | Arizona State University | INFOCOM | 2021 | [PUB] [PDF] |
| Dual Attention-Based Federated Learning for Wireless Traffic Prediction | King Abdullah University of Science and Technology | INFOCOM | 2021 | [PUB] [PDF] [CODE] |
| FedSens: A Federated Learning Approach for Smart Health Sensing with Class Imbalance in Resource Constrained Edge Computing | Universität Notre Dame | INFOCOM | 2021 | [PUB] |
| P-FedAvg: Parallelizing Federated Learning with Theoretical Guarantees | SYSU; Guangdong Key Laboratory of Big Data Analysis and Processing | INFOCOM | 2021 | [PUB] |
| Meta-HAR: Federated Representation Learning for Human Activity Recognition. | University of Alberta | WWW | 2021 | [PUB] [PDF] [CODE] |
| PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization | PKU | WWW | 2021 | [PUB] [PDF] [CODE] |
| Communication Efficient Federated Generalized Tensor Factorization for Collaborative Health Data Analytics | Emory | WWW | 2021 | [PUB] [CODE] |
| Hierarchical Personalized Federated Learning for User Modeling | USTC | WWW | 2021 | [PUB] |
| Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data | PKU | WWW | 2021 | [PUB] [PDF] [SLIDE] [CODE] |
| Incentive Mechanism for Horizontal Federated Learning Based on Reputation and Reverse Auction | SYSU | WWW | 2021 | [PUB] |
| Physical-Layer Arithmetic for Federated Learning in Uplink MU-MIMO Enabled Wireless Networks. | Nanjing University | INFOCOM | 2020 | [PUB] |
| Optimizing Federated Learning on Non-IID Data with Reinforcement Learning | Universität von Toronto | INFOCOM | 2020 | [PUB] [SLIDE] [CODE] [解读] |
| Enabling Execution Assurance of Federated Learning at Untrusted Participants | DO | 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 | Wuhan University | INFOCOM | 2019 | [PUB] [PDF] [UC.] |
| InPrivate Digging: Enabling Tree-based Distributed Data Mining with Differential Privacy | Collaborative Innovation Center of Geospatial Technology | INFOCOM | 2018 | [PUB] |
Federated Learning papers accepted by top Database conference and journal, including OSDI(USENIX Symposium on Operating Systems Design and Implementation), SOSP(Symposium on Operating Systems Principles), ISCA(International Symposium on Computer Architecture), MLSys(Conference on Machine Learning and Systems), EuroSys(European Conference on Computer Systems), TPDS(IEEE Transactions on Parallel and Distributed Systems), DAC(Design Automation Conference), TOCS(ACM Transactions on Computer Systems), TOS(ACM Transactions on Storage), TCAD(IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems), TC(IEEE Transactions on Computers).
| Titel | Zugehörigkeit | Veranstaltungsort | Jahr | Materialien |
|---|---|---|---|---|
| AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems. | DAC | 2024 | [PUB] | |
| Fake Node-Based Perception Poisoning Attacks against Federated Object Detection Learning in Mobile Computing Networks | DAC | 2024 | [PUB] | |
| Flagger: Cooperative Acceleration for Large-Scale Cross-Silo Federated Learning Aggregation | ISCA | 2024 | [PUB] | |
| FedTrans: Efficient Federated Learning via Multi-Model Transformation | UIUC | MLSys | 2024 | [PUB] [PDF] |
| LIFL: A Lightweight, Event-driven Serverless Platform for Federated Learning | UC Riverside | MLSys | 2024 | [PUB] [PDF] |
| HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning | Korea University | MLSys | 2024 | [PUB] [PDF] [CODE] |
| DeTA: Minimizing Data Leaks in Federated Learning via Decentralized and Trustworthy Aggregation | IBM Research | EuroSys | 2024 | [PUB] |
| FLOAT: Federated Learning Optimizations with Automated Tuning | 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 | DO | 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 | SCHLAG | TCAD | 2024 | [PUB] |
| BSR-FL: An Efficient Byzantine-Robust Privacy-Preserving Federated Learning Framework | TC | 2024 | [PUB] | |
| User-Distribution-Aware Federated Learning for Efficient Communication and Fast Inference | ECNU; SHU | TC | 2024 | [PUB] |
| FedRFQ: Prototype-Based Federated Learning With Reduced Redundancy, Minimal Failure, and Enhanced Quality | SDU | TC | 2024 | [PUB] [PDF] |
| Value of Information: A Comprehensive Metric for Client Selection in Federated Edge Learning | SDU | TC | 2024 | [PUB] |
| Age-Aware Data Selection and Aggregator Placement for Timely Federated Continual Learning in Mobile Edge Computing | DLUT | TC | 2024 | [PUB] |
| FedGKD: Toward Heterogeneous Federated Learning via Global Knowledge Distillation | HUST | TC | 2024 | [PUB] [PDF] |
| Digital Twin-Assisted Federated Learning Service Provisioning Over Mobile Edge Networks | SDU | TC | 2024 | [PUB] |
| REFL: Resource-Efficient Federated Learning | QMUL | EuroSys | 2023 | [PUB] [PDF] [CODE] |
| A First Look at the Impact of Distillation Hyper-Parameters in Federated Knowledge Distillation | EuroSys workshop | 2023 | [PUB] | |
| Towards Practical Few-shot Federated NLP | EuroSys workshop | 2023 | [PUB] | |
| Can Fair Federated Learning Reduce the need for Personalisation? | EuroSys workshop | 2023 | [PUB] | |
| Gradient-less Federated Gradient Boosting Tree with Learnable Learning Rates | EuroSys workshop | 2023 | [PUB] | |
| Towards Robust and Bias-free Federated Learning | EuroSys workshop | 2023 | [PUB] | |
| FedTree: A Federated Learning System For Trees | UC Berkeley | MLSys | 2023 | [PUB] [CODE] |
| FLINT: A Platform for Federated Learning Integration | MLSys | 2023 | [PUB] [PDF] | |
| On Noisy Evaluation in Federated Hyperparameter Tuning | CMU | MLSys | 2023 | [PUB] [PDF] [CODE] |
| GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning | UBC | MLSys | 2023 | [PUB] [PDF] [CODE] |
| Self-Supervised On-Device Federated Learning From Unlabeled Streams. | FDU | TCAD | 2023 | [PUB] [PDF] |
| Optimizing Training Efficiency and Cost of Hierarchical Federated Learning in Heterogeneous Mobile-Edge Cloud Computing | ECNU | TCAD | 2023 | [PUB] |
| Lightweight Blockchain-Empowered Secure and Efficient Federated Edge Learning | University of Exeter | TC | 2023 | [PUB] |
| Towards Data-Independent Knowledge Transfer in Model-Heterogeneous Federated Learning | PolyU | TC | 2023 | [PUB] |
| A New Federated Scheduling Algorithm for Arbitrary-Deadline DAG Tasks | NEFU | TC | 2023 | [PUB] |
| Privacy-Enhanced Decentralized Federated Learning at Dynamic Edge | SDU | TC | 2023 | [PUB] |
| Byzantine-Resilient Federated Learning at Edge | SDU | TC | 2023 | [PUB] [PDF] |
| PrivAim: A Dual-Privacy Preserving and Quality-Aware Incentive Mechanism for Federated Learning | CSU | TC | 2023 | [PUB] |
| Accelerating Federated Learning With a Global Biased Optimiser | University of Exeter | TC | 2023 | [PUB] [PDF] [CODE] |
| Type-Aware Federated Scheduling for Typed DAG Tasks on Heterogeneous Multicore Platforms | TU Dortmund University | TC | 2023 | [PUB] [CODE] |
| Sandbox Computing: A Data Privacy Trusted Sharing Paradigm Via Blockchain and Federated Learning. | BUPT | TC | 2023 | [PUB] |
| CHEESE: Distributed Clustering-Based Hybrid Federated Split Learning Over Edge Networks | SUDA | TPDS | 2023 | [PUB] |
| Hierarchical Federated Learning With Momentum Acceleration in Multi-Tier Networks | 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 | University of Exeter | TPDS | 2023 | [PUB] [PDF] [CODE] |
| DRFL: Federated Learning in Diabetic Retinopathy Grading Using Fundus Images | National Institute of Technology Silchar | TPDS | 2023 | [PUB] |
| FedProf: Selective Federated Learning Based on Distributional Representation Profiling | Peng Cheng Laboratory | TPDS | 2023 | [PUB] [PDF] [UC] |
| Federated Ensemble Model-Based Reinforcement Learning in Edge Computing | University of Exeter | TPDS | 2023 | [PUB] [PDF] |
| Incentive Mechanism Design for Joint Resource Allocation in Blockchain-Based Federated Learning. | IUPUI | TPDS | 2023 | [PUB] [PDF] |
| HiFlash: Communication-Efficient Hierarchical Federated Learning With Adaptive Staleness Control and Heterogeneity-Aware Client-Edge Association. | SYSU | TPDS | 2023 | [PUB] [PDF] |
| From Deterioration to Acceleration: A Calibration Approach to Rehabilitating Step Asynchronism in Federated Optimization. | PolyU | TPDS | 2023 | [PUB] [PDF] [CODE] |
| Federated Learning Over Coupled Graphs | XJTU | TPDS | 2023 | [PUB] [PDF] |
| Privacy vs. Efficiency: Achieving Both Through Adaptive Hierarchical Federated Learning | NUDT | TPDS | 2023 | [PUB] |
| On Model Transmission Strategies in Federated Learning With Lossy Communications | SZU | TPDS | 2023 | [PUB] |
| Scheduling Algorithms for Federated Learning With Minimal Energy Consumption | University of Bordeaux | TPDS | 2023 | [PUB] [PDF] [CODE] |
| Auction-Based Cluster Federated Learning in Mobile Edge Computing Systems | SCHLAG | 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. | SCHLAG | 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. | ICH S | TPDS | 2023 | [PUB] |
| GossipFL: A Decentralized Federated Learning Framework With Sparsified and Adaptive Communication. | HKBU | TPDS | 2023 | [PUB] |
| FedMDS: An Efficient Model Discrepancy-Aware Semi-Asynchronous Clustered Federated Learning Framework. | CQU | TPDS | 2023 | [PUB] |
| HierFedML: Aggregator Placement and UE Assignment for Hierarchical Federated Learning in Mobile Edge Computing. | DUT | TPDS | 2023 | [PUB] |
| Data selection for efficient model update in federated learning | EuroSys workshop | 2022 | [PUB] | |
| Empirical analysis of federated learning in heterogeneous environments | EuroSys workshop | 2022 | [PUB] | |
| BAFL: A Blockchain-Based Asynchronous Federated Learning Framework | TC | 2022 | [PUB] [CODE] | |
| L4L: Experience-Driven Computational Resource Control in Federated Learning | TC | 2022 | [PUB] | |
| Adaptive Federated Learning on Non-IID Data With Resource Constraint | TC | 2022 | [PUB] | |
| Locking Protocols for Parallel Real-Time Tasks With Semaphores Under Federated Scheduling. | TCAD | 2022 | [PUB] | |
| Client Scheduling and Resource Management for Efficient Training in Heterogeneous IoT-Edge Federated Learning | ECNU | TCAD | 2022 | [PUB] |
| PervasiveFL: Pervasive Federated Learning for Heterogeneous IoT Systems. | ECNU | TCAD | 2022 | [PUB] |
| FHDnn: communication efficient and robust federated learning for AIoT networks | UC San Diego | DAC | 2022 | [PUB] |
| A Decentralized Federated Learning Framework via Committee Mechanism With Convergence Guarantee | SYSU | TPDS | 2022 | [PUB] [PDF] |
| Improving Federated Learning With Quality-Aware User Incentive and Auto-Weighted Model Aggregation | DO | 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 | BISSCHEN | 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. | DO | TPDS | 2022 | [PUB] |
| DONE: Distributed Approximate Newton-type Method for Federated Edge Learning. | University of Sydney | TPDS | 2022 | [PUB] [PDF] [CODE] |
| Flexible Clustered Federated Learning for Client-Level Data Distribution Shift. | CQU | TPDS | 2022 | [PUB] [PDF] [CODE] |
| Min-Max Cost Optimization for Efficient Hierarchical Federated Learning in Wireless Edge Networks. | Xidian University | TPDS | 2022 | [PUB] |
| LightFed: An Efficient and Secure Federated Edge Learning System on Model Splitting. | CSU | TPDS | 2022 | [PUB] |
| On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Federated Learning. | Purdue | TPDS | 2022 | [PUB] [PDF] [CODE] |
| Incentive-Aware Autonomous Client Participation in Federated Learning. | Sun Yat-sen University | TPDS | 2022 | [PUB] |
| Communicational and Computational Efficient Federated Domain Adaptation. | HKUST | TPDS | 2022 | [PUB] |
| Decentralized Edge Intelligence: A Dynamic Resource Allocation Framework for Hierarchical Federated Learning. | NTU | TPDS | 2022 | [PUB] |
| Differentially Private Byzantine-Robust Federated Learning. | Qufu Normal University | TPDS | 2022 | [PUB] |
| Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing. | University of Exeter | TPDS | 2022 | [PUB] [PDF] [CODE] |
| Reputation-Aware Hedonic Coalition Formation for Efficient Serverless Hierarchical Federated Learning. | BUAA | TPDS | 2022 | [PUB] |
| Differentially Private Federated Temporal Difference Learning. | Stony Brook University | TPDS | 2022 | [PUB] |
| Towards Efficient and Stable K-Asynchronous Federated Learning With Unbounded Stale Gradients on Non-IID Data. | XJTU | TPDS | 2022 | [PUB] [PDF] |
| Communication-Efficient Federated Learning With Compensated Overlap-FedAvg. | SCU | TPDS | 2022 | [PUB] [PDF] [CODE] |
| PAPAYA: Practical, Private, and Scalable Federated Learning. | Meta AI | MLSys | 2022 | [PUB] [PDF] |
| LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning | USC | MLSys | 2022 | [PUB] [PDF] [CODE] |
| Accelerated Training via Device Similarity in Federated Learning | EuroSys workshop | 2021 | [PUB] | |
| Towards Federated Learning with Attention Transfer to Mitigate System and Data Heterogeneity of Clients | EuroSys workshop | 2021 | [PUB] | |
| Towards Mitigating Device Heterogeneity in Federated Learning via Adaptive Model Quantization | EuroSys workshop | 2021 | [PUB] | |
| SAFA: A Semi-Asynchronous Protocol for Fast Federated Learning With Low Overhead | University of Warwick | TC | 2021 | [PDF] [PUB] [CODE] |
| Efficient Federated Learning for Cloud-Based AIoT Applications | ECNU | TCAD | 2021 | [PUB] |
| HADFL: Heterogeneity-aware Decentralized Federated Learning Framework | USTC | DAC | 2021 | [PDF] [PUB] |
| Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration. | GMU | DAC | 2021 | [PDF] [PUB] |
| FedLight: Federated Reinforcement Learning for Autonomous Multi-Intersection Traffic Signal Control. | ECNU | DAC | 2021 | [PUB] |
| Oort: Efficient Federated Learning via Guided Participant Selection | Universität von Michigan | OSDI | 2021 | [PUB] [PDF] [CODE] [SLIDES] [VIDEO] |
| Towards Efficient Scheduling of Federated Mobile Devices Under Computational and Statistical Heterogeneity. | Old Dominion University | TPDS | 2021 | [PUB] [PDF] |
| Self-Balancing Federated Learning With Global Imbalanced Data in Mobile Systems. | CQU | TPDS | 2021 | [PUB] [CODE] |
| An Efficiency-Boosting Client Selection Scheme for Federated Learning With Fairness Guarantee | SCUT | TPDS | 2021 | [PUB] [PDF] [解读] |
| Proof of Federated Learning: A Novel Energy-Recycling Consensus Algorithm. | Beijing Normal University | TPDS | 2021 | [PUB] [PDF] |
| Biscotti: A Blockchain System for Private and Secure Federated Learning. | UBC | TPDS | 2021 | [PUB] |
| Mutual Information Driven Federated Learning. | Deakin University | TPDS | 2021 | [PUB] |
| Accelerating Federated Learning Over Reliability-Agnostic Clients in Mobile Edge Computing Systems. | University of Warwick | TPDS | 2021 | [PUB] [PDF] |
| FedSCR: Structure-Based Communication Reduction for Federated Learning. | HKU | TPDS | 2021 | [PUB] |
| FedScale: Benchmarking Model and System Performance of Federated Learning | Universität von 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).
| Titel | Zugehörigkeit | Veranstaltungsort | Jahr | Materialien |
|---|---|---|---|---|
| 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 | Kneipe | |
| Federated Machine Learning as a Self-Adaptive Problem | SEAMS@ICSE workshop | 2021 | Kneipe |
This section partially refers to DBLP search engine and repositories Awesome-Federated-Learning-on-Graph-and-GNN-papers and Awesome-Federated-Machine-Learning.
| Titel | Zugehörigkeit | Veranstaltungsort | Jahr | Materialien |
|---|---|---|---|---|
| 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. Graph. ? | 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. Graph. ? | 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. Big Data | 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 | ICH S | 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 | DO | Naturkommunikation | 2022 | [PUB] [CODE] [解读] |
| Malicious Transaction Identification in Digital Currency via Federated Graph Deep Learning | BISSCHEN | 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. | Big Data | 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 |
