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A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guarantee. (arXiv:2108.00365v2 [cs.LG] UPDATED)
Sept. 9, 2022, 1:20 a.m. | Chunjiang Che, Xiaoli Li, Chuan Chen, Xiaoyu He, Zibin Zheng
cs.CR updates on arXiv.org arxiv.org
Federated learning allows multiple participants to collaboratively train an
efficient model without exposing data privacy. However, this distributed
machine learning training method is prone to attacks from Byzantine clients,
which interfere with the training of the global model by modifying the model or
uploading the false gradient. In this paper, we propose a novel serverless
federated learning framework Committee Mechanism based Federated Learning
(CMFL), which can ensure the robustness of the algorithm with convergence
guarantee. In CMFL, a committee system …
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