[36] Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1-2):1-210, 2021.
[37] Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh. Scaffold: Stochastic controlled averaging for federated learning. In Proceedings of the ICML, pages 5132-5143. PMLR, 2020.
[38] Robert E Kass and Larry Wasserman. The selection of prior distributions by formal rules. J AM STAT ASSOC, 91(435):1343-1370, 1996.
[39] A Krizhevsky. Learning multiple layers of features from tiny images. Master's thesis, University of Tront, 2009.
[40] Frederik Kunstner, Raunak Kumar, and Mark Schmidt. Homeomorphic-invariance of em: Non-asymptotic convergence in  $\mathrm{kl}$  divergence for exponential families via mirror descent. In Proceedings of the AISTATS, pages 3295-3303. PMLR, 2021.
[41] L. Le Cam. Maximum Likelihood: An Introduction. INT STAT REV, 58(2):153-171, 1990. Wiley, ISI.
[42] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.
[43] Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu, and Bingsheng He. A survey on federated learning systems: Vision, hype and reality for data privacy and protection. IEEE T KNOWL DATA EN, 35(4):3347-3366, 2023.
[44] Tian Li, Shengyuan Hu, Ahmad Beirami, and Virginia Smith. Ditto: Fair and robust federated learning through personalization. In Proceedings of the ICML, pages 6357-6368. PMLR, 2021.
[45] Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. Federated Optimization in Heterogeneous Networks. Proceedings of MLSys, 2:429-450, March 2020.
[46] Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang. On the Convergence of FedAvg on Non-IID Data. In Proceedings of the ICLR, April 2020.
[47] Zexi Li, Xinyi Shang, Rui He, Tao Lin, and Chao Wu. No fear of classifier biases: Neural collapse inspired federated learning with synthetic and fixed classifier. arXiv:2303.10058, 2023.
[48] Liangxi Liu, Feng Zheng, Hong Chen, Guo-Jun Qi, Heng Huang, and Ling Shao. A bayesian federated learning framework with online laplace approximation. arXiv:2102.01936, 2021.
[49] Guodong Long, Yue Tan, Jing Jiang, and Chengqi Zhang. Federated Learning for Open Banking. In Qiang Yang, Lixin Fan, and Han Yu, editors, Federated Learning: Privacy and Incentive, pages 240-254. Springer International Publishing, Cham, 2020.
[50] Christos Louizos, Matthias Reisser, Joseph Soriaga, and Max Welling. An expectation-maximization perspective on federated learning. arXiv:2111.10192, 2021.
[51] Andrew L Maas, Awni Y Hannun, Andrew Y Ng, et al. Rectifier nonlinearities improve neural network acoustic models. In Proceedings of the ICML, volume 30, page 3. Citeseer, 2013.
[52] Othmane Marfoq, Giovanni Neglia, Aurélien Bellet, Laetitia Kameni, and Richard Vidal. Federated Multi-Task Learning under a Mixture of Distributions. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, and J. Wortman Vaughan, editors, Proceedings of the NeurIPS, volume 34, pages 15434–15447, 2021.
[53] Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In Proceedings of the AISTATS, pages 1273–1282. PMLR, 2017.