Dual-Layer FL and Blockchain Empowered High Accurate Edge Training Framework

Author:

Wang Xinyan,Hu An,Jia Jingli,Du Jiacheng,Ning Yongjie,Zhu Ying

Publisher

Springer Nature Singapore

Reference11 articles.

1. Haque, M.M., Sarker, S., Dewan, M.A.A.: Driving maneuver classification from time series data: a rule-based machine learning approach. Appl Intell (2022)

2. Wu, W., He, L., Lin, W., Mao, R.: Accelerating federated learning over reliability-agnostic clients in mobile edge computing systems. IEEE Trans. Parallel Distrib. Syst. 32(7), 1539–1551 (2021)

3. Xu, G., Li, H., Liu, S., Yang, K., Lin, X.: VerifyNet: secure and verifiable federated learning. IEEE Trans. Inf. Forens. Secur. 15, 911–926 (2020)

4. McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B. A.: Communication-efficient learning of deep networks from decentralized data. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, pp. 1273–1282 (2017)

5. Samarakoon, S., Bennis, M., Saad, W. Debbah, M.: Federated learning for ultra-reliable low-latency V2V communications. In: 2018 IEEE Global Communications Conference (GLOBECOM), pp. 1–7 (2018)

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