Review of Mathematical Optimization in Federated Learning (CSIAM Transactions on Applied Mathematics, 2025)
Federated learning (FL) has been becoming a popular interdisciplinary research area in both applied mathematics and information sciences. Mathematically, FLaims to collaboratively optimize aggregate objective functions over distributed datasets while satisfying a variety of privacy and system constraints. Different from conventional distributed optimization methods, FL needs to address several specific issues(e.g. non-i.i.d. data and differential private noises), which pose a set of new challengesin the problem formulation, algorithm design, and convergence analysis. In this paper, we will systematically review existing FL optimization research including theirassumptions, formulations, methods, and theoretical results. Potential future directions are also discussed.