  Due to communication constraints and intermittent client availability in
federated learning, only a subset of clients can participate in each training
round. While most prior works assume uniform and unbiased client selection,
recent work on biased client selection has shown that selecting clients with
higher local losses can improve error convergence speed. However, previously
proposed biased selection strategies either require additional communication
cost for evaluating the exact local loss or utilize stale local loss, which can
even make the model diverge. In this paper, we present a bandit-based
communication-efficient client selection strategy UCB-CS that achieves faster
convergence with lower communication overhead. We also demonstrate how client
selection can be used to improve fairness.
