# Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization

arXiv: https://arxiv.org/abs/2311.00944 · HTML: https://mlsanity.com/paper/2311.00944/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 48 references that are in the map.
- First posted on arXiv 2 Nov 2023.

## Builds on (most cited first, 30 of 31)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Lojasiewicz Condition](https://mlsanity.com/paper/1608.04636/) (2016)
- [On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems](https://mlsanity.com/paper/1906.00331/) (2019)
- [Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning](https://mlsanity.com/paper/2101.11203/) (2021)
- [Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods](https://mlsanity.com/paper/1902.08297/) (2019)
- [Distributionally Robust Federated Averaging](https://mlsanity.com/paper/2102.12660/) (2021)
- [Robust Federated Learning: The Case of Affine Distribution Shifts](https://mlsanity.com/paper/2006.08907/) (2020)
- [Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems](https://mlsanity.com/paper/2001.03724/) (2020)
- [FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning](https://mlsanity.com/paper/2207.14130/) (2022)
- [Weakly-Convex Concave Min-Max Optimization: Provable Algorithms and Applications in Machine Learning](https://mlsanity.com/paper/1810.02060/) (2018)
- [Stochastic AUC Maximization with Deep Neural Networks](https://mlsanity.com/paper/1908.10831/) (2019)
- [FedNest: Federated Bilevel, Minimax, and Compositional Optimization](https://mlsanity.com/paper/2205.02215/) (2022)
- [A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems](https://mlsanity.com/paper/2010.15768/) (2020)
- [Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity](https://mlsanity.com/paper/2112.05604/) (2021)
- [Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiency](https://mlsanity.com/paper/2102.13152/) (2021)
- [Federated Minimax Optimization: Improved Convergence Analyses and Algorithms](https://mlsanity.com/paper/2203.04850/) (2022)
- [Stochastic Hamiltonian Gradient Methods for Smooth Games](https://mlsanity.com/paper/2007.04202/) (2020)
- [Single-Timescale Stochastic Nonconvex-Concave Optimization for Smooth Nonlinear TD Learning](https://mlsanity.com/paper/2008.10103/) (2020)
- [SAPD+: An Accelerated Stochastic Method for Nonconvex-Concave Minimax Problems](https://mlsanity.com/paper/2205.15084/) (2022)
- [Efficient Algorithms for Federated Saddle Point Optimization](https://mlsanity.com/paper/2102.06333/) (2021)
- [A Proximal Alternating Direction Method of Multiplier for Linearly Constrained Nonconvex Minimization](https://mlsanity.com/paper/1812.10229/) (2018)
- [Local Linear Convergence of Forward-Backward under Partial Smoothness](https://mlsanity.com/paper/1407.5611/) (2014)
- [Solving a Class of Non-Convex Minimax Optimization in Federated Learning](https://mlsanity.com/paper/2310.03613/) (2023)
- [A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning](https://mlsanity.com/paper/2206.01132/) (2022)
- [Federated Minimax Optimization with Client Heterogeneity](https://mlsanity.com/paper/2302.04249/) (2023)
- [Protecting Privacy through Distributed Computation in Multi-agent Decision Making](https://mlsanity.com/paper/1402.0589/) (2014)
- [Adaptive Federated Minimax Optimization with Lower Complexities](https://mlsanity.com/paper/2211.07303/) (2022)

## Built on by (most cited first, 4 of 4)

- [Federated Stochastic Minimax Optimization under Heavy-Tailed Noises](https://mlsanity.com/paper/2511.04456/) (2025)
- [Federated Instrumental Variable Analysis via Federated Generalized Method of Moments](https://mlsanity.com/paper/2505.21012/) (2025)
- [Stability and Generalization for Distributed SGDA](https://mlsanity.com/paper/2411.09365/) (2024)
- [Communication-Efficient Agnostic Federated Learning via Faster Convergence and Compression](https://mlsanity.com/paper/2609.36610/) (2026)

## 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.