2024/05/07 by Chris Junchi Li, Li, Chris Junchi · 1 citation
Computer Science · Engineering · #Blind Source Separation Techniques #Distributed #Distributed Control Multi-Agent Systems #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Parallel #Sparse and Compressive Sensing Techniques #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2405.04566
openalex publication_date 2024/05/07 · openalex created_date 2024/05/11 · openalex updated_date 2026/07/28
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 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. K-GT-Minimax's ability to handle data heterogeneity and ensure robustness underscores its significance in advancing federated learning research and applications.