[45] D. Maclaurin, D. Duvenaud, and R. Adams. Gradient-based hyperparameter optimization through reversible learning. In International conference on machine learning, pages 2113-2122. PMLR, 2015.
[46] P. Manoharan, R. Walia, C. Iwendi, T. A. Ahanger, S. Suganthi, M. Kamruzzaman, S. Bourouis, W. Alhakami, and M. Hamdi. SVM-based generative adversarial networks for federated learning and edge computing attack model and outpoising. Expert Systems, page e13072, 2022.
[47] B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pages 1273-1282. PMLR, 2017.
[48] A. Mitra, R. Jaafar, G. J. Pappas, and H. Hassani. Linear convergence in federated learning: Tackling client heterogeneity and sparse gradients. Advances in Neural Information Processing Systems, 34:14606-14619, 2021.
[49] M. Mohri, G. Sivek, and A. T. Suresh. Agnostic federated learning. In International Conference on Machine Learning, pages 4615-4625. PMLR, 2019.
[50] R. Pathak and M. J. Wainwright. Fedsplit: An algorithmic framework for fast federated optimization. Advances in neural information processing systems, 33:7057-7066, 2020.
[51] F. Pedregosa. Hyperparameter optimization with approximate gradient. In International conference on machine learning, pages 737-746. PMLR, 2016.
[52] A. Rajeswaran, C. Finn, S. M. Kakade, and S. Levine. Meta-learning with implicit gradients. Advances in neural information processing systems, 32, 2019.
[53] S. Reddi, Z. Charles, M. Zaheer, Z. Garrett, K. Rush, J. Konečný, S. Kumar, and H. B. McMahan. Adaptive federated optimization. arXiv preprint arXiv:2003.00295, 2020.
[54] Y. Roh, K. Lee, S. E. Whang, and C. Suh. Fairbatch: Batch selection for model fairness. In International Conference on Learning Representations, 2021.
[55] S. Sabach and S. Shtern. A first order method for solving convex bilevel optimization problems. SIAM Journal on Optimization, 27(2):640-660, 2017.
[56] A. Shaban, C.-A. Cheng, N. Hatch, and B. Boots. Truncated back-propagation for bilevel optimization. In The 22nd International Conference on Artificial Intelligence and Statistics, pages 1723-1732. PMLR, 2019.
[57] P. Sharma, R. Panda, and G. Joshi. Federated minimax optimization with client heterogeneity. arXiv preprint arXiv:2302.04249, 2023.
[58] H. Shen and T. Chen. On penalty-based bilevel gradient descent method. arXiv preprint arXiv:2302.05185, 2023.
[59] C. Shi, J. Lu, and G. Zhang. An extended kuhn-tucker approach for linear bilevel programming. Applied Mathematics and Computation, 162(1):51-63, 2005.
[60] R. Shokri and V. Shmatikov. Privacy-preserving deep learning. In Proceedings of the 22nd ACM SIGSAC conference on computer and communications security, pages 1310-1321, 2015.
[61] A. Sinha, P. Malo, and K. Deb. A review on bilevel optimization: From classical to evolutionary approaches and applications. IEEE Transactions on Evolutionary Computation, 22(2):276-295, 2017.
[62] D. Sow, K. Ji, Z. Guan, and Y. Liang. A constrained optimization approach to bilevel optimization with multiple inner minima. arXiv preprint arXiv:2203.01123, 2022.
[63] S. U. Stich. Local sgd converges fast and communicates little. arXiv preprint arXiv:1805.09767, 2018.