Abstract
Data sharing plays a crucial role in the Internet of Vehicles, as it greatly enhances the driving experience for users. Federated Learning (FL) has shown good advantages and efficiency in knowledge sharing among vehicles. However, due to the uncertainty of the IoVs, the existing federated learning frameworks cannot meet the high-precision, fast convergence, and high fault tolerance requirements in the learning process. To address these issues, this paper proposes a hierarchical federated learning framework for IoVs environment that combines synchronous and asynchronous methods to improve machine learning performance in the Internet of Vehicles environment. The proposed asynchronous algorithm can improve the accuracy of the global model via controlling the proportion of parameters submitted by users. In addition, to improve the reliability of the parameters, our framework provides a malicious node exclusion algorithm to improve the reliability of the parameters. It effectively reduces the adverse impact of malicious parameters on the global model. Finally, lightweight pseudonym is used in the proposed framework to ensure the privacy of participants’ identities. The experimental results demonstrate that the proposed framework achieves high learning accuracy and fast convergence speed. Additionally, it effectively defends against poisoning attacks and ensures the protection of participants’ identity privacy.
This research was funded in part by the National Natural Science Foundation of China under Grant U20B2049 and U20B2046, and in part by the Key Research and Development Project of Sichuan Province of China under Grant 2022YFG0172.
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References
Ma, Y., Wang, Z., Yang, H., Yang, L.: Artificial intelligence applications in the development of autonomous vehicles: a survey. IEEE/CAA J. Automatica Sinica 7(2), 315–329 (2020)
McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282. PMLR (2017)
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)
Kong, X., et al.: A federated learning-based license plate recognition scheme for 5G-enabled internet of vehicles. IEEE Trans. Industr. Inf. 17(12), 8523–8530 (2021)
Lyu, L., et al.: Towards fair and privacy-preserving federated deep models. IEEE Trans. Parallel Distrib. Syst. 31(11), 2524–2541 (2020)
Xie, C., Koyejo, S., Gupta, I.: Asynchronous federated optimization. arXiv preprint arXiv:1903.03934 (2019)
Lian, X., Zhang, W., Zhang, C., Liu, J.: Asynchronous decentralized parallel stochastic gradient descent. In: International Conference on Machine Learning, pp. 3043–3052. PMLR (2018)
Huang, J., et al.: AFLPC: an asynchronous federated learning privacy-preserving computing model applied to 5G–V2X. Secur. Commun. Netw. 2022 (2022)
Nishiyama, H., Ito, M., Kato, N.: Relay-by-smartphone: realizing multihop device- to-device communications. IEEE Commun. Mag. 52(4), 56–65 (2014)
Dai, Y., Xu, D., Maharjan, S., Chen, Z., He, Q., Zhang, Y.: Blockchain and deep reinforcement learning empowered intelligent 5G beyond. IEEE Netw. 33(3), 10–17 (2019)
Lu, Y., Huang, X., Dai, Y., Maharjan, S., Zhang, Y.: Federated learning for data privacy preservation in vehicular cyber-physical systems. IEEE Netw. 34(3), 50–56 (2020)
Kang, J., Xiong, Z., Niyato, D., Xie, S., Zhang, J.: Incentive mechanism for reliable federated learning: a joint optimization approach to combining reputation and contract theory. IEEE Internet Things J. 6(6), 10700–10714 (2019)
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. IEEE (2018)
Kim, H., Park, J., Bennis, M., Kim, S.L.: Blockchained on-device federated learning. IEEE Commun. Lett. 24(6), 1279–1283 (2019)
Feyzmahdavian, H.R., Aytekin, A., Johansson, M.: An asynchronous mini-batch algorithm for regularized stochastic optimization. IEEE Trans. Autom. Control 61(12), 3740–3754 (2016)
Lian, X., Zhang, W., Zhang, C., Liu, J.: Asynchronous decentralized parallel stochastic gradient descent. In: International Conference on Machine Learning, pp. 3043–3052. PMLR (2018)
Lin, C., He, D., Huang, X., Kumar, N., Choo, K.K.R.: BCPPA: a blockchain-based conditional privacy-preserving authentication protocol for vehicular ad hoc networks. IEEE Trans. Intell. Transp. Syst. 22(12), 7408–7420 (2021)
Blanchard, P., Mhamdi, E., Guerraoui, R., Stainer, J.: Machine learning with adversaries: byzantine tolerant gradient descent. In: Neural Information Processing Systems (2017)
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Zhou, R., Niu, X., Xiong, L., Wang, Y., Zhao, Y., Yu, K. (2024). A Hierarchical Asynchronous Federated Learning Privacy-Preserving Framework for IoVs. In: Yang, H., Lu, R. (eds) Frontiers in Cyber Security. FCS 2023. Communications in Computer and Information Science, vol 1992. Springer, Singapore. https://doi.org/10.1007/978-981-99-9331-4_7
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