Fast Heterogeneous Federated Learning with Hybrid Client Selection

• URL: http://arxiv.org/abs/2208.05135v1
• Date: Wed, 10 Aug 2022 04:10:24 GMT
• Title: Fast Heterogeneous Federated Learning with Hybrid Client Selection
• Authors: Guangyuan Shen, Dehong Gao, DuanXiao Song, libin yang, Xukai Zhou, Shirui Pan, Wei Lou, Fang Zhou
• Abstract summary: We present a novel clustering-based client selection scheme to accelerate the convergence by variance reduction. We also present the tighter convergence guarantee of the proposed scheme thanks to the variance reduction.
• Score: 29.902439962022868
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Client selection schemes are widely adopted to handle the communication-efficient problems in recent studies of Federated Learning (FL). However, the large variance of the model updates aggregated from the randomly-selected unrepresentative subsets directly slows the FL convergence. We present a novel clustering-based client selection scheme to accelerate the FL convergence by variance reduction. Simple yet effective schemes are designed to improve the clustering effect and control the effect fluctuation, therefore, generating the client subset with certain representativeness of sampling. Theoretically, we demonstrate the improvement of the proposed scheme in variance reduction. We also present the tighter convergence guarantee of the proposed method thanks to the variance reduction. Experimental results confirm the exceed efficiency of our scheme compared to alternatives.

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