[41] Zhuqing Liu, Xin Zhang, Songtao Lu, and Jia Liu. Precision: Decentralized constrained min-max learning with low communication and sample complexities. arXiv preprint arXiv:2303.02532, 2023.
[42] Luo Luo, Haishan Ye, Zhichao Huang, and Tong Zhang. Stochastic recursive gradient descent ascent for stochastic nonconvex-strongly-concave minimax problems. Advances in Neural Information Processing Systems, 33:20566-20577, 2020.
[43] Pouria Mahdavinia, Yuyang Deng, Haochuan Li, and Mehrdad Mahdavi. Tight analysis of extra-gradient and optimistic gradient methods for nonconvex minimax problems. Advances in Neural Information Processing Systems, 35:31213-31225, 2022.
[44] Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pages 1273-1282. PMLR, 2017.
[45] Maher Nouiehed, Maziar Sanjabi, Tianjian Huang, Jason D Lee, and Meisam Razaviyayn. Solving a class of non-convex min-max games using iterative first order methods. Advances in Neural Information Processing Systems, 32, 2019.
[46] H Rafique, M Liu, Q Lin, and T Yang. Non-convex min-max optimization: provable algorithms and applications in machine learning. arXiv preprint arXiv:1810.02060, 2018.
[47] Mohammad Rasouli, Tao Sun, and Ram Rajagopal. Fedgan: Federated generative adversarial networks for distributed data. arXiv preprint arXiv:2006.07228, 2020.
[48] Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečny, Sanjiv Kumar, and H Brendan McMahan. Adaptive federated optimization. arXiv preprint arXiv:2003.00295, 2020.
[49] Amirhossein Reisizadeh, Farzan Farnia, Ramtin Pedarsani, and Ali Jabbabaie. Robust federated learning: The case of affine distribution shifts. Advances in Neural Information Processing Systems, 33:21554-21565, 2020.
[50] Pranay Sharma, Rohan Panda, Gauri Joshi, and Pramod Varshney. Federated minimax optimization: Improved convergence analyses and algorithms. In International Conference on Machine Learning, pages 19683-19730. PMLR, 2022.
[51] Pranay Sharma, Rohan Panda, and Gauri Joshi. Federated minimax optimization with client heterogeneity. arXiv preprint arXiv:2302.04249, 2023.
[52] Zhenyu Sun and Ermin Wei. A communication-efficient algorithm with linear convergence for federated minimax learning. arXiv preprint arXiv:2206.01132, 2022.
[53] Davoud Ataee Tarzanagh, Mingchen Li, Christos Thrampoulidis, and Samet Oymak. Fednest: Federated bilevel, minimax, and compositional optimization. In International Conference on Machine Learning, pages 21146-21179. PMLR, 2022.
[54] Kiran K, Thekumparamil, Prateek Jain, Praneeth Netrapalli, and Swoong Oh. Efficient algorithms for smooth minimax optimization. Advances in Neural Information Processing Systems, 32, 2019.
[55] Hoi-To Wai, Mingyi Hong, Zhuoran Yang, Zhaoran Wang, and Kexin Tang. Variance reduced policy evaluation with smooth function approximation. Advances in Neural Information Processing Systems, 32: 5784-5795, 2019.
[56] Jingkang Wang, Tianyun Zhang, Sijia Liu, Pin-Yu Chen, Jiacen Xu, Makan Fardad, and Bo Li. Adversarial attack generation empowered by min-max optimization. Advances in Neural Information Processing Systems, 34:16020-16033, 2021.
[57] Xidong Wu, Zhengmian Hu, and Heng Huang. Decentralized riemannian algorithm for nonconvex minimax problems. arXiv preprint arXiv:2302.03825, 2023.
[58] Xidong Wu, Feihu Huang, Zhengmian Hu, and Heng Huang. Faster adaptive federated learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 10379-10387, 2023.
[59] Xidong Wu, Jianhui Sun, Zhengmian Hu, Junyi Li, Aidong Zhang, and Heng Huang. Federated conditional stochastic optimization. Advances in Neural Information Processing Systems (NeurIPS), 36, 2023.
[60] Yihan Wu, Hongyang Zhang, and Heng Huang. Retrievalguard: Provably robust 1-nearest neighbor image retrieval. In International Conference on Machine Learning, pages 24266-24279. PMLR, 2022.