Decentralized Minimax Optimization with Local Updates and Gradient Tracking for Robust Federated Learning

The proposed Dec-FedTrack algorithm employs local updates and gradient tracking to enable robust and communication-efficient decentralized minimax optimization, addressing the challenges of data heterogeneity and adversarial robustness in federated learning.