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In recent years, federated minimax optimization has attracted growinginterest due to its extensive applications in various machine learning tasks.While Smoothed Alternative Gradient Descent Ascent (Smoothed-AGDA) has provedits success in centralized nonconvex minimax optimization, how and whethersmoothing technique could be helpful in federated setting remains unexplored.In this paper, we propose a new algorithm termed Federated Stochastic SmoothedGradient Descent Ascent (FESS-GDA), which utilizes the smoothing technique forfederated minimax optimization. We prove that FESS-GDA can be uniformly used tosolve several classes of federated minimax problems and prove new or betteranalytical convergence results for these settings. We showcase the practicalefficiency of FESS-GDA in practical federated learning tasks of traininggenerative adversarial networks (GANs) and fair classification.

Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization