Client Selection in Federated Learning: Principles, Challenges, and Opportunities

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 article, we systematically present recent advances in the…

• NS
  National Science Foundation

  Awards: USDA/NIFA 2020-67021-32855, IIS-1838207, OIA-2134901, CNS 1901218