  As a privacy-preserving paradigm for training Machine Learning (ML) models,
Federated Learning (FL) has received tremendous attention from both industry
and academia. In a typical FL scenario, clients exhibit significant
heterogeneity in terms of data distribution and hardware configurations. Thus,
randomly sampling clients in each training round may not fully exploit the
local updates from heterogeneous clients, resulting in lower model accuracy,
slower convergence rate, degraded fairness, etc. To tackle the FL client
heterogeneity problem, various client selection algorithms have been developed,
showing promising performance improvement. In this paper, we systematically
present recent advances in the emerging field of FL client selection and its
challenges and research opportunities. We hope to facilitate practitioners in
choosing the most suitable client selection mechanisms for their applications,
as well as inspire researchers and newcomers to better understand this exciting
research topic.
