Year: 2025
Author: Shusen Yang, Fangyuan Zhao, Zihao Zhou, Liang Shi, Xuebin Ren, Zongben Xu
CSIAM Transactions on Applied Mathematics, Vol. 6 (2025), Iss. 2 : pp. 207–249
Abstract
Federated learning (FL) has been becoming a popular interdisciplinary research area in both applied mathematics and information sciences. Mathematically, FL aims 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 challenges in the problem formulation, algorithm design, and convergence analysis. In this paper, we will systematically review existing FL optimization research including their assumptions, formulations, methods, and theoretical results. Potential future directions are also discussed.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/csiam-am.SO-2024-0023
CSIAM Transactions on Applied Mathematics, Vol. 6 (2025), Iss. 2 : pp. 207–249
Published online: 2025-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 43
Keywords: Federated learning distributed optimization convergence analysis error bounds.
Author Details
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