  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.
