1. [1] Z. Lian, W. Wang, and C. Su, “COFEL: Communication-Efficient and optimized federated learning with local differential privacy,” 2021 IEEE International Conference on Communications (ICC): Communication and Information Systems Security Symposium (IEEE ICC'21-CISS Symposium), Montreal, Canada, June 2021. 10.1109/icc42927.2021.9500632
2. [2] B. McMahan, E. Moore, D. Ramage, S. Hampson, and B.A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” Artificial Intelligence and Statistics, pp.1273-1282, 2017.
3. [3] L. Wang, W. Wang, and B. Li, “Cmfl: Mitigating communication overhead for federated learning,” 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp.954-964, IEEE, 2019. 10.1109/icdcs.2019.00099
4. [4] M. Mohri, G. Sivek, and A.T. Suresh, “Agnostic federated learning,” arXiv preprint arXiv:1902.00146, 2019.
5. [5] A. Hard, K. Rao, R. Mathews, S. Ramaswamy, F. Beaufays, S. Augenstein, H. Eichner, C. Kiddon, and D. Ramage, “Federated learning for mobile keyboard prediction,” 2018.