Fast Heterogeneous Federated Learning with Hybrid Client Selection. (arXiv:2208.05135v2 [cs.LG] UPDATED)

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, …

arxiv client federated learning hybrid learning lg