Abstract
Internet of Vehicles (IoV) enables the integration of smart vehicles with Internet and collaborative analysis from shared data among vehicles. Machine learning technologies show significant advantages and efficiency for data analysis in IoV. However, the user data could be sensitive in nature, and the reliability and efficiency of sharing these data is hard to guarantee. Moreover, due to the intermittent and unreliable communications of various distributed vehicles, the traditional machine learning algorithms are not suitable for heterogeneous IoV network. In this paper, we propose a novel reputation mechanism framework that integrates the IoV with blockchain and federated learning named RepBFL. In this framework, blockchain is used to protect the shared data between the vehicles. The Road Side Units (RSU) select the high reputation vehicular nodes to share their data for federated learning. To enhance the security and reliability of the data sharing process, we develop the reputation calculated mechanism to evaluate the reliability of all vehicles in IoV. The proposed framework is feasible for the large heterogeneous vehicular networks and perform the collaborative data analysis in distributed vehicles. The experimental results show that the proposed approach can improve the data sharing efficiency. Furthermore, the reputation mechanism is able to deal with malicious behaviors effectively.
Supported in part by the Fundamental Research Funds for the Central Universities(2021YJS304) and 2020 Industrial Internet Innovation and Development Project.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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, AISTATS 2017, Fort Lauderdale, FL, USA, vol. 54, pp. 1273–1282. PMLR (2017)
Mollah, M.B., et al.: Blockchain for the internet of vehicles towards intelligent transportation systems: a survey. IEEE Internet Things J. 8(6), 4157–4185 (2021)
Xiao, L., et al.: A reinforcement learning and blockchain-based trust mechanism for edge networks. IEEE Trans. Commun. 68(9), 5460–5470 (2020)
Chai, H., Leng, S., Chen, Y., Zhang, K.: A hierarchical blockchain-enabled federated learning algorithm for knowledge sharing in internet of vehicles. IEEE Trans. Intell. Transp. Syst. 22(7), 3975–3986 (2021)
Zou, Y., Shen, F., Yan, F., Lin, J., Qiu, Y.: Reputation-based regional federated learning for knowledge trading in blockchain-enhanced IOV. In: 2021 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6. IEEE (2021)
Liu, L., et al.: Blockchain-enabled secure data sharing scheme in mobile-edge computing: an asynchronous advantage actor-critic learning approach. IEEE Internet Things J. 8(4), 2342–2353 (2021)
Erdemir, E.N., Dragotti, P.L., Gündüz, D.: Privacy-aware time-series data sharing with deep reinforcement learning. IEEE Trans. Inf. Forensics Secur. 16, 389–401 (2021)
Cao, X., Fang, M., Liu, J., Gong, N.Z.: Fltrust: Byzantine-robust federated learning via trust bootstrapping. In: 28th Annual Network and Distributed System Security Symposium, NDSS 2021, virtually, 21–25 February 2021. The Internet Society (2021)
Zhang, J., Wu, Y., Pan, R.: Incentive mechanism for horizontal federated learning based on reputation and reverse auction. In: Proceedings of the Web Conference 2021, pp. 947–956 (2021)
Warnat-Herresthal, S., et al.: Swarm learning for decentralized and confidential clinical machine learning. Nature 594(7862), 265–270 (2021)
Wei, K., et al.: Federated learning with differential privacy: algorithms and performance analysis. IEEE Trans. Inf. Forensics Secur. 15, 3454–3469 (2020)
Hu, R., Guo, Y., Li, H., Pei, Q., Gong, Y.: Personalized federated learning with differential privacy. IEEE Internet Things J. 7(10), 9530–9539 (2020)
Wang, S., Huang, X., Yu, R., Zhang, Y., Hossain, E.: Permissioned blockchain for efficient and secure resource sharing in vehicular edge computing. arXiv preprint arXiv:1906.06319 (2019)
Yang, Z., Yang, K., Lei, L., Zheng, K., Leung, V.C.: Blockchain-based decentralized trust management in vehicular networks. IEEE Internet Things J. 6(2), 1495–1505 (2018)
Jiang, T., Fang, H., Wang, H.: Blockchain-based internet of vehicles: distributed network architecture and performance analysis. IEEE Internet Things J. 6(3), 4640–4649 (2018)
Kang, J., Yu, R., Huang, X., Wu, M., Maharjan, S., Xie, S., Zhang, Y.: Blockchain for secure and efficient data sharing in vehicular edge computing and networks. IEEE Internet Things J. 6(3), 4660–4670 (2018)
Luo, B., Li, X., Weng, J., Guo, J., Ma, J.: Blockchain enabled trust-based location privacy protection scheme in vanet. IEEE Trans. Veh. Technol. 69(2), 2034–2048 (2019)
Lu, Y., Huang, X., Zhang, K., Maharjan, S., Zhang, Y.: Blockchain empowered asynchronous federated learning for secure data sharing in internet of vehicles. IEEE Trans. Veh. Technol. 69(4), 4298–4311 (2020)
Gai, K., Wu, Y., Zhu, L., Zhang, Z., Qiu, M.: Differential privacy-based blockchain for industrial Internet-of-Things. IEEE Trans. Ind. Inform. 16(6), 4156–4165 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, H. et al. (2022). RepBFL: Reputation Based Blockchain-Enabled Federated Learning Framework for Data Sharing in Internet of Vehicles. In: Shen, H., et al. Parallel and Distributed Computing, Applications and Technologies. PDCAT 2021. Lecture Notes in Computer Science(), vol 13148. Springer, Cham. https://doi.org/10.1007/978-3-030-96772-7_50
Download citation
DOI: https://doi.org/10.1007/978-3-030-96772-7_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-96771-0
Online ISBN: 978-3-030-96772-7
eBook Packages: Computer ScienceComputer Science (R0)