  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. To tackle this
tradeoff, we in this paper formulate the client selection problem under joint
consideration of effective participation and fairness.} Further, we propose
E3CS, a stochastic client selection scheme to solve the problem, and we
corroborate its effectiveness by conducting real data-based experiments.
According to our experimental results, the proposed selection scheme is able to
achieve up to 2x faster convergence to a fixed model accuracy while maintaining
the same level of final model accuracy, compared with the state-of-the-art
selection schemes.
