1. Konecny, J., McMahan, H.B., Yu, F.X., Richtarik, P., Suresh, A.T., Bacon, D.: Federated learning: Strategies for improving communication efficiency. In: NIPS Workshop on Private Multi-Party Machine Learning (2016)
2. Reisizadeh, A., Mokhtari, A., Hassani, H., Jadbabaie, A., Pedarsani, R.: Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization. In: International Conference on Artificial Intelligence and Statistics, pp. 2021–2031 (2020)
3. Liang, F., Yang, Q., Liu, R., Wang, J., Sato, K., Guo, J.: Semi-synchronous federated learning protocol with dynamic aggregation in internet of vehicles. IEEE Trans. Veh. Technol. 71(5), 4677–4691 (2022)
4. Kong, X., Wang, K., Hou, M., Hao, X., Shen, G., Chen, X., Xia, F.: A federated learning-based license plate recognition scheme for 5g-enabled internet of vehicles. IEEE Trans. Industr. Inf. 17(12), 8523–8530 (2021)
5. Ayaz, F., Sheng, Z., Tian, D., Guan, Y.L.: A blockchain based federated learning for message dissemination in vehicular networks. IEEE Trans. Veh. Technol. 71(2), 1927–1940 (2022)