1. Q. Yang, L. Fan, H. Yu (Eds.), Federated Learning: Privacy and Incentive, Vol. 12500, Springer Nature, 2020. doi:https://doi.org/10.1007/978-3-030-63076-8.
2. Q. Li, Z. Wen, B. He, Federated learning systems: Vision, hype and reality for data privacy and protection, ArXiv abs/1907.09693.
3. B. McMahan, E. Moore, D. Ramage, S. Hampson, B.A. y. Arcas, Communication-Efficient Learning of Deep Networks from Decentralized Data, in: A. Singh, J. Zhu (Eds.), Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Vol. 54 of Proceedings of Machine Learning Research, PMLR, 2017, pp. 1273–1282. https://proceedings.mlr.press/v54/mcmahan17a.html.
4. N. Rodríguez, G. Stipcich, D. Jiménez, J. Ruiz-Millán, E. Martínez-Cámara, G. González-Seco, M. Luzon, M. Veganzones, F. Herrera, Federated learning and differential privacy: Software tools analysis, the sherpa.ai fl framework and methodological guidelines for preserving data privacy, Information Fusion 64. doi:10.1016/j.inffus.2020.07.009.
5. Federated learning: Challenges, methods, and future directions;Li;IEEE Signal Processing Magazine,2020