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 has proved its success in centralized nonconvex minimax optimization%2C how and whether smoothing technique could be helpful in federated setting remains unexplored. In this paper%2C we propose a new algorithm termed Federated Stochastic Smoothed Gradient Descent Ascent (FESS-GDA)%2C which utilizes the smoothing technique for federated minimax optimization. We prove that FESS-GDA can be uniformly used to solve several classes of federated minimax problems and prove new or better analytical convergence results for these settings. We showcase the practical efficiency of FESS-GDA in practical federated learning tasks of training generative adversarial networks (GANs) and fair classification.?quality=80&w=800)
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