1. Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. Found. Trends Mach. Learn. 14(1–2), 1–210 (2021)
2. Liu, J., Huang, J., Zhou, Y., Li, X., Ji, S., Xiong, H., Dou, D.: From distributed machine learning to federated learning: a survey. Knowl. Inf. Syst. 64(4), 885–917 (2022)
3. McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282. PMLR (2017)
4. McMahan, B., Ramage, D.: Federated learning: collaborative machine learning without centralized training data (2017). https://ai.googleblog.com/2017/04/federated-learning-collaborative.html
5. Naik, D., Naik, N.: The changing landscape of machine learning: a comparative analysis of centralized machine learning, distributed machine learning and federated machine learning. In: UK Workshop on Computational Intelligence (UKCI). Springer (2023)