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This content will become publicly available on December 30, 2024

Title: Federated Minimax Optimization with Client Heterogeneity
Minimax optimization has seen a surge in interest with the advent of modern applications such as GANs, and it is inherently more challenging than simple minimization. The difficulty is exacerbated by the training data residing at multiple edge devices or clients, especially when these clients can have heterogeneous datasets and heterogeneous local computation capabilities. We propose a general federated minimax optimization framework that subsumes such settings and several existing methods like Local SGDA. We show that naive aggregation of model updates made by clients running unequal number of local steps can result in optimizing a mismatched objective function – a phenomenon previously observed in standard federated minimization. To fix this problem, we propose normalizing the client updates by the number of local steps. We analyze the convergence of the proposed algorithm for classes of nonconvex-concave and nonconvex-nonconcave functions and characterize the impact of heterogeneous client data, partial client participation, and heterogeneous local computations. For all the function classes considered, we significantly improve the existing computation and communication complexity results. Experimental results support our theoretical claims.  more » « less
Award ID(s):
2045694
NSF-PAR ID:
10489317
Author(s) / Creator(s):
; ;
Publisher / Repository:
Transactions of Machine Learning Research
Date Published:
Journal Name:
Transactions on machine learning research
ISSN:
2835-8856
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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