  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.
