Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization

arXiv:2311.00944v2 Announce Type: replace-cross
Abstract: In recent years, federated minimax optimization has attracted growing interest due to its extensive applications in various machine learning tasks. While Smoothed Alternative Gradient Descent Ascent (Smoothed-AGDA) has proved its success in centralized nonconvex minimax optimization, how and whether smoothing technique could be helpful in federated setting remains unexplored. In this paper, we propose a new algorithm termed Federated Stochastic Smoothed Gradient Descent Ascent (FESS-GDA), which utilizes the smoothing technique for federated minimax optimization. We …

abstract applications arxiv cs.it cs.lg gradient machine machine learning math.it math.oc minimax optimization stat.ml stochastic success tasks type