Fast Decentralized Gradient Tracking for Federated Minimax Optimization with Local Updates

• URL: http://arxiv.org/abs/2405.04566v1
• Date: Tue, 7 May 2024 17:25:56 GMT
• Title: Fast Decentralized Gradient Tracking for Federated Minimax Optimization with Local Updates
• Authors: Chris Junchi Li,
• Abstract summary: textttK-GT-Minimax's ability to handle data heterogeneity underscores its significance in advancing federated learning research and applications.
• Score: 5.269633789700637
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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