[24] Antti Koskela, Joonas Jälkö, Lukas Prediger, and Antti Honkela. Tight differential privacy for discrete-valued mechanisms and for the subsampled gaussian mechanism using FFT. In International Conference on Artificial Intelligence and Statistics, pages 3358-3366. PMLR, 2021.
[25] Chao Li, Michael Hay, Vibhor Rastogi, Gerome Miklau, and Andrew McGregor. Optimizing linear counting queries under differential privacy. In Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, pages 123-134, 2010.
[26] Chao Li, Gerome Miklau, Michael Hay, Andrew Mcgregor, and Vibhor Rastogi. The matrix mechanism: optimizing linear counting queries under differential privacy. The VLDB Journal, 24:757-781, 2015.
[27] Aurelien Lucchi, Frank Proske, Antonio Orvieto, Francis Bach, and Hans Kersting. On the theoretical properties of noise correlation in stochastic optimization. Neural Information Processing Systems, 2022.
[28] Horia Mania, Xinghao Pan, Dimitris Papailiopoulos, Benjamin Recht, Kannan Ramchandran, and Michael I. Jordan. Perturbed iterate analysis for asynchronous stochastic optimization. SIAM Journal on Optimization, 27(4):2202-2229, 2017. doi: 10.1137/16M1057000. URL https://doi.org/10.1137/16M1057000.
[29] Ryan McKenna, Gerome Miklau, Michael Hay, and Ashwin Machanavajjhala. Optimizing error of high-dimensional statistical queries under differential privacy. arXiv preprint arXiv:1808.03537, 2018.
[30] Ryan McKenna, Gerome Miklau, Michael Hay, and Ashwin Machanavajjhala. Hdmm: Optimizing error of high-dimensional statistical queries under differential privacy. arXiv preprint arXiv:2106.12118, 2021.
[31] Brendan McMahan and Abhradeep Thakurta. Federated learning with formal differential privacy guarantees. Google AI Blog, 2022.
[32] 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.
[33] Konstantin Mishchenko, Ahmed Khaled, and Peter Richtárik. Random reshuffling: Simple analysis with vast improvements. Advances in Neural Information Processing Systems, 33: 17309-17320, 2020.
[34] Aritra Mitra, Rayana Jaafar, George J Pappas, and Hamed Hassani. Linear convergence in federated learning: Tackling client heterogeneity and sparse gradients. Advances in Neural Information Processing Systems, 34:14606-14619, 2021.
[35] Yurii Nesterov. A method for solving the convex programming problem with convergence rate  $o(1 / k^2)$ . Proceedings of the USSR Academy of Sciences, 269:543-547, 1983.
[36] John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Mike Rabbat, Mani Malek, and Dzmitry Huba. Federated learning with buffered asynchronous aggregation. In International Conference on Artificial Intelligence and Statistics, pages 3581-3607. PMLR, 2022.
[37] Antonio Orvieto, Hans Kersting, Frank Proske, Francis Bach, and Aurelien Lucchi. Anticorrelated noise injection for improved generalization. arXiv preprint arXiv:2202.02831, 2022.
[38] Antonio Orvieto, Anant Raj, Hans Kersting, and Francis Bach. Explicit regularization in overparametrized models via noise injection. arXiv preprint arXiv:2206.04613, 2022.
[39] B.T. Polyak. Some methods of speeding up the convergence of iteration methods. *USSR Computational Mathematics and Mathematical Physics*, 4(5):1-17, 1964. ISSN 0041-5553. doi: https://doi.org/10.1016/0041-5553(64)90137-5. URL https://www.sciencedirect.com/science/article/pii/0041555364901375.