1. Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., He, B.: A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection pp. 1–41 (2019). http://arxiv.org/abs/1907.09693
2. Bonawitz K, Eichner H, Grieskamp W, Huba D, Ingerman A, Ivanov V, Kiddon C, Konecny J, Mazzocchi S, McMahan HB (2019) Others: Towards federated learning at scale: System design. arXiv preprint arXiv:1902.01046
3. Kairouz P, McMahan HB, Avent B, Bellet A, Bennis M, Bhagoji AN, Bonawitz K, Charles Z, Cormode G, Cummings R et al (2019) Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977
4. Saputra YM, Hoang DT, Nguyen DN, Dutkiewicz E, Mueck MD, Srikanteswara S (2019) Energy demand prediction with federated learning for electric vehicle networks. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp. 1–6. IEEE
5. Wang X, Han Y, Wang C, Zhao Q, Chen X, Chen M (2019) In-edge ai: Intelligentizing mobile edge computing, caching and communication by federated learning. IEEE Netw 33(5):156–165