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Throughput-Optimal Topology Design for Cross-Silo Federated Learning.

, , , и . NeurIPS, (2020)

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SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization., , , , , и . AISTATS, том 238 из Proceedings of Machine Learning Research, стр. 3457-3465. PMLR, (2024)A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates., , , и . J. Mach. Learn. Res., (2023)Evaluating the Energy Consumption of Machine Learning: Systematic Literature Review and Experiments., , , , и . CoRR, (2024)Throughput-Optimal Topology Design for Cross-Silo Federated Learning., , , и . NeurIPS, (2020)Personalized Federated Learning through Local Memorization., , , и . ICML, том 162 из Proceedings of Machine Learning Research, стр. 15070-15092. PMLR, (2022)On The Impact of Client Sampling on Federated Learning Convergence., , , и . CoRR, (2021)A General Theory for Client Sampling in Federated Learning., , , и . FL@IJCAI, том 13448 из Lecture Notes in Computer Science, стр. 46-58. Springer, (2022)Federated Multi-Task Learning under a Mixture of Distributions., , , , и . NeurIPS, стр. 15434-15447. (2021)Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning., , , и . ICML, том 139 из Proceedings of Machine Learning Research, стр. 3407-3416. PMLR, (2021)Federated Learning for Data Streams., , , и . AISTATS, том 206 из Proceedings of Machine Learning Research, стр. 8889-8924. PMLR, (2023)