[38] L. F. O. Chamon, S. Paternain, M. Calvo-Fullana, and A. Ribeiro, “Constrained learning with non-convex losses,” IEEE Transactions on Information Theory, vol. 69, no. 3, pp. 1739–1760, 2023.
[39] S. Boyd, S. P. Boyd, and L. Vandenberghe, Convex optimization. Cambridge university press, 2004.
[40] J. F. Bonnans and A. Shapiro, “Optimization problems with perturbations: A guided tour,” SIAM review, vol. 40, no. 2, pp. 228–264, 1998.
[41] M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of machine learning. MIT press, 2018.
[42] R. Ge, J. D. Lee, and T. Ma, “Learning one-hidden-layer neural networks with landscape design,” in International Conference on Learning Representations, 2018.
[43] A. Brutzkus and A. Globerson, "Globally optimal gradient descent for a convnet with gaussian inputs," in International Conference on Machine Learning, 2017, pp. 605-614.
[44] M. Soltanolkotabi, A. Javanmard, and J. D. Lee, “Theoretical insights into the optimization landscape of over-parameterized shallow neural networks,” IEEE Transactions on Information Theory, vol. 65, no. 2, pp. 742–769, 2018.
[45] R. Shokri and V. Shmatikov, "Privacy-preserving deep learning," in Proceedings of the 22nd ACM SIGSAC conference on computer and communications security, 2015, pp. 1310-1321.
[46] T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, “Federated optimization in heterogeneous networks,” Proceedings of Machine learning and systems, vol. 2, pp. 429–450, 2020.
[47] H. Wang, Z. Kaplan, D. Niu, and B. Li, “Optimizing federated learning on non-iid data with reinforcement learning,” in IEEE INFOCOM 2020-IEEE Conference on Computer Communications. IEEE, 2020, pp. 1698–1707.
[48] Y. LeCun, C. Cortes, and C. Burges, "Mnist handwritten digit database," ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist, vol. 2, 2010.
[49] H. Xiao, K. Rasul, and R. Vollgraf, "Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms," 2017.
[50] R. T. Rockafellar, Conjugate duality and optimization. SIAM, 1974.
[51] D. P. Bertsekas, “Nonlinear programming,” Journal of the Operational Research Society, vol. 48, no. 3, pp. 334–334, 1997.
[52] D. Bertsekas, Convex optimization theory. Athena Scientific, 2009, vol. 1.
[53] R. T. Rockafellar, Convex analysis. Princeton university press, 1997, vol. 11.
[54] D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.
[55] S. Boyd, L. Xiao, and A. Mutapcic, "Subgradient methods," lecture notes of EE392o, Stanford University, Autumn Quarter, vol. 2004, pp. 2004-2005, 2003.
[56] 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. PMLR, 2017, pp. 1273-1282.
[57] X. Zhang, M. Hong, S. V. Dhople, W. Yin, and Y. Liu, "Fedpd: A federated learning framework with adaptivity to non-iid data," IEEE Transactions on Signal Processing, vol. 69, pp. 6055-6070, 2020.
[58] D. A. E. Acar, Y. Zhao, R. M. Navarro, M. Mattina, P. N. Whatmough, and V. Saligrama, "Federated learning based on dynamic regularization," arXiv preprint arXiv:2111.04263, 2021.
[59] A. Krizhevsky, “Learning multiple layers of features from tiny images,” Canadian Institute for Advanced Research, Tech. Rep., 2009.
[60] L. Wang, S. Xu, X. Wang, and Q. Zhu, “Addressing class imbalance in federated learning,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 11, 2021, pp. 10 165–10 173.