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Federated Learning for Data Streams.

, , , and . AISTATS, volume 206 of Proceedings of Machine Learning Research, page 8889-8924. PMLR, (2023)

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A Cautionary Tale: On the Role of Reference Data in Empirical Privacy Defenses., , , , and . CoRR, (2023)FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings., , , , , , , , , and 14 other author(s). NeurIPS, (2022)Federated Learning under Heterogeneous and Correlated Client Availability., , , , and . INFOCOM, page 1-10. IEEE, (2023)Throughput-Optimal Topology Design for Cross-Silo Federated Learning., , , and . NeurIPS, (2020)Personalized Federated Learning through Local Memorization., , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 15070-15092. PMLR, (2022)Count-Min Sketch with Conservative Updates: Worst-Case Analysis., and . CoRR, (2024)Federated Multi-Task Learning under a Mixture of Distributions., , , , and . NeurIPS, page 15434-15447. (2021)Federated Learning for Data Streams., , , and . AISTATS, volume 206 of Proceedings of Machine Learning Research, page 8889-8924. PMLR, (2023)Tackling heterogeneity in federated learning systems. (Surmonter l'hétérogénéité dans les systèmes d'apprentissage fédéré).. Côte d'Azur University, Nice, France, (2023)