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Award ID contains: 2033671

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  1. ; ; ; ; ; ; ; ; ; ; et al (, npj Unconventional Computing)
  2. ; ; ; ; (, International Conference on Machine Learning (ICML))
    Free, publicly-accessible full text available July 31, 2026
  3. ; ; ; (, International Conference on Machine Learning (ICML))
    Free, publicly-accessible full text available July 31, 2026
  4. ; ; ; (, International Conference on Artificial Intelligence and Statistics (AISTATS))
    Free, publicly-accessible full text available May 3, 2026
  5. ; ; ; ; (, International Conference on Artificial Intelligence and Statistics (AISTATS))
    Free, publicly-accessible full text available May 3, 2026
  6. ; ; ; (, International Conference on Learning Representations (ICLR))
    Free, publicly-accessible full text available April 24, 2026
  7. ; ; ; (, Findings of the Association for Computational Linguistics: ACL 2024)
    Accepted Manuscript Full Text Available
  8. ; ; (, Proceedings of the 41st International Conference on Machine Learning (ICML))
    Accepted Manuscript Full Text Available
  9. ; ; ; (, Proceedings of The 27th International Conference on Artificial Intelligence and Statistics)
    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 successful in centralized nonconvex minimax optimization, how and whether smoothing techniques could be helpful in a 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 applied 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. 
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    Accepted Manuscript Full Text Available
  10. ; ; ; (, Transactions on machine learning research)
    Accepted Manuscript Full Text Available