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arXiv · 2405.04566

Fast Decentralized Gradient Tracking for Federated Minimax Optimization with Local Updates

Chris Junchi Li
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Abstract

Federated learning (FL) for minimax optimization has emerged as a powerful paradigm for training models across distributed nodes/clients while preserving data privacy and model robustness on data heterogeneity. In this work, we delve into the decentralized implementation of federated minimax optimization by proposing \texttt{K-GT-Minimax}, a novel decentralized minimax optimization algorithm that combines local updates and gradient tracking techniques. Our analysis showcases the algorithm's communication efficiency and convergence rate for nonconvex-strongly-concave (NC-SC) minimax optimization, demonstrating a superior convergence rate compared to existing methods. \texttt{K-GT-Minimax}'s ability to handle data heterogeneity and ensure robustness underscores its significance in advancing federated learning research and applications.

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BibTeXRIS

Chris Junchi Li. 2024-05-07. Fast Decentralized Gradient Tracking for Federated Minimax Optimization with Local Updates. https://arxiv.org/abs/2405.04566

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