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2020

Stochastic Client Selection for Federated Learning with Volatile Clients

Huang, Tiansheng, Lin, Weiwei, Shen, Li et al.

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Federated Learning (FL), arising as a privacy-preserving machine learning paradigm, has received notable attention from the public.

In each round of synchronous FL training, only a fraction of available clients are chosen to participate, and the selection decision might have a significant effect on the training efficiency, as well as the final model performance.
In this paper, we investigate the client selection problem under a volatile context, in which the local training of heterogeneous clients is likely to fail due to various kinds of reasons and in different levels of frequency.
{\color{black}Intuitively, too much training failure might potentially reduce the training efficiency, while too much selection on clients with greater stability might introduce bias, thereby resulting in degradation of the training effectiveness.

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