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A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates., , , and . J. Mach. Learn. Res., (2023)SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization., , , , , and . AISTATS, volume 238 of Proceedings of Machine Learning Research, page 3457-3465. PMLR, (2024)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)A General Theory for Client Sampling in Federated Learning., , , and . FL@IJCAI, volume 13448 of Lecture Notes in Computer Science, page 46-58. Springer, (2022)On The Impact of Client Sampling on Federated Learning Convergence., , , and . CoRR, (2021)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)Free-rider Attacks on Model Aggregation in Federated Learning., , and . AISTATS, volume 130 of Proceedings of Machine Learning Research, page 1846-1854. PMLR, (2021)Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning., , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 3407-3416. PMLR, (2021)