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CFL: Cluster Federated Learning in Large-scale Peer-to-Peer Networks. (arXiv:2204.03843v2 [cs.CR] UPDATED)
July 7, 2022, 1:20 a.m. | Qian Chen, Zilong Wang, Yilin Zhou, Jiawei Chen, Dan Xiao, Xiaodong Lin
cs.CR updates on arXiv.org arxiv.org
Federated learning (FL) has sparked extensive interest in exploiting the
private data on clients' local devices. However, the parameter server setting
of FL not only has high bandwidth requirements, but also poses data privacy
issues and a single point of failure. In this paper, we propose an efficient
and privacy-preserving protocol, dubbed CFL, which is the first fine-grained
global model training for FL in large-scale peer-to-peer (P2P) networks. Unlike
previous FL in P2P networks, CFL aggregates local model update parameters …
cluster federated learning large networks peer-to-peer scale
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