most citedFIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations

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cs.LG2025

Towards Optimal Heterogeneous Client Sampling in Multi-Model Federated Learning

Haoran Zhang, Zejun Gong, Zekai Li +3

Federated learning (FL) allows edge devices to collaboratively train models without sharing local data. As FL gains popularity, clients may need to train multiple unrelated FL mode…