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Fast-Convergent Federated Learning via Cyclic Aggregation. (arXiv:2210.16520v1 [cs.LG])
Nov. 1, 2022, 1:11 a.m. | Youngjoon Lee, Sangwoo Park, Joonhyuk Kang
cs.LG updates on arXiv.org arxiv.org
Federated learning (FL) aims at optimizing a shared global model over
multiple edge devices without transmitting (private) data to the central
server. While it is theoretically well-known that FL yields an optimal model --
centrally trained model assuming availability of all the edge device data at
the central server -- under mild condition, in practice, it often requires
massive amount of iterations until convergence, especially under presence of
statistical/computational heterogeneity. This paper utilizes cyclic learning
rate at the server side …
More from arxiv.org / cs.LG updates on arXiv.org
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