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Client selection schemes are widely adopted to handle thecommunication-efficient problems in recent studies of Federated Learning (FL).However, the large variance of the model updates aggregated from therandomly-selected unrepresentative subsets directly slows the FL convergence.We present a novel clustering-based client selection scheme to accelerate theFL convergence by variance reduction. Simple yet effective schemes are designedto 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 invariance reduction. We also present the tighter convergence guarantee of theproposed method thanks to the variance reduction. Experimental results confirmthe exceed efficiency of our scheme compared to alternatives.

Fast Heterogeneous Federated Learning with Hybrid Client Selection