Fast Decentralized Gradient Tracking for Federated Minimax Optimization with Local Updates
<|reference_start|>Fast Decentralized Gradient Tracking for Federated Minimax Optimization with Local Updates: 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.<|reference_end|>
@article{li2024fast,
    title={Fast Decentralized Gradient Tracking for Federated Minimax Optimization
  with Local Updates},
    author={Chris Junchi Li},
    journal={arXiv preprint arXiv:2405.04566},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04566},
    primaryClass={cs.LG cs.DC stat.ML}
}