Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866913321781821440 |
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| author | Shen, Wei Huang, Minhui Zhang, Jiawei Shen, Cong |
| author_facet | Shen, Wei Huang, Minhui Zhang, Jiawei Shen, Cong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_00944 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization Shen, Wei Huang, Minhui Zhang, Jiawei Shen, Cong Machine Learning Information Theory Optimization and Control 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. |
| title | Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization |
| topic | Machine Learning Information Theory Optimization and Control |
| url | https://arxiv.org/abs/2311.00944 |