mlsanityData to 7 Oct 2026

Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization

arXiv 2311.00944 ↗First posted 2 Nov 2023stat.ML cs.IT cs.LGcited by 4

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Abstract (arXiv)

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 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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 31 of its 48 references that are in the map. First posted on arXiv 2 Nov 2023.

Builds on

31 of 48 references.

References · 31

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19 more
  1. 2022FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learningcited by 62
  2. 2019Stochastic AUC Maximization with Deep Neural Networkscited by 61
  3. 2022FedNest: Federated Bilevel, Minimax, and Compositional Optimizationcited by 60
  4. 2020A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problemscited by 57
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  9. 2020Single-Timescale Stochastic Nonconvex-Concave Optimization for Smooth Nonlinear TD Learningcited by 24
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  12. 2018A Proximal Alternating Direction Method of Multiplier for Linearly Constrained Nonconvex Minimizationcited by 14
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  14. 2023Solving a Class of Non-Convex Minimax Optimization in Federated Learningcited by 10
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  19. 2019SNAP: Finding Approximate Second-Order Stationary Solutions Efficiently for Non-convex Linearly Constrained Problemscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2025Federated Instrumental Variable Analysis via Federated Generalized Method of Momentscited by 1
  2. 2025Federated Stochastic Minimax Optimization under Heavy-Tailed Noisescited by 1
  3. 2024Stability and Generalization for Distributed SGDAcited by 0
  4. 2026Communication-Efficient Agnostic Federated Learning via Faster Convergence and Compressioncited by 0