Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization. (arXiv:2311.00944v1 [stat.ML])

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 prove that FESS-GDA can be …

applications arxiv gradient machine machine learning minimax optimization stochastic success tasks