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.

提出了一种名为 K-GT-Minimax 的新型去中心化的最小最大优化算法，结合了本地更新和梯度跟踪技术，证明了该算法在非凸强凹（NC-SC）最小最大优化问题上具有较高的收敛速度，能够处理数据异构性并确保鲁棒性，从而推动联邦学习研究和应用的发展。