RepBFL: Reputation Based Blockchain-Enabled Federated Learning Framework for Data Sharing in Internet of Vehicles
Authors: 
Haoyu Chen, Naiyue Chen, He Liu, Honglei Zhang, Jiabo Xu, Huaping Chen, Yidong LiAuthors Info & Claims
Haoyu Chen, Naiyue Chen, He Liu, Honglei Zhang, Jiabo Xu, Huaping Chen, Yidong LiAuthors Info & ClaimsPages 536 - 547
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.
References
[1]
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)
[2]
Mollah MB et al. Blockchain for the internet of vehicles towards intelligent transportation systems: a survey IEEE Internet Things J. 2021 8 6 4157-4185
[3]
Xiao L et al. A reinforcement learning and blockchain-based trust mechanism for edge networks IEEE Trans. Commun. 2020 68 9 5460-5470
[4]
Chai H, Leng S, Chen Y, and Zhang K A hierarchical blockchain-enabled federated learning algorithm for knowledge sharing in internet of vehicles IEEE Trans. Intell. Transp. Syst. 2021 22 7 3975-3986
[5]
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)
[6]
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. 2021 8 4 2342-2353
[7]
Erdemir EN, Dragotti PL, and Gündüz D Privacy-aware time-series data sharing with deep reinforcement learning IEEE Trans. Inf. Forensics Secur. 2021 16 389-401
[8]
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)
[9]
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)
[10]
Warnat-Herresthal S et al. Swarm learning for decentralized and confidential clinical machine learning Nature 2021 594 7862 265-270
[11]
Wei K et al. Federated learning with differential privacy: algorithms and performance analysis IEEE Trans. Inf. Forensics Secur. 2020 15 3454-3469
[12]
Hu R, Guo Y, Li H, Pei Q, and Gong Y Personalized federated learning with differential privacy IEEE Internet Things J. 2020 7 10 9530-9539
[13]
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)
[14]
Yang Z, Yang K, Lei L, Zheng K, and Leung VC Blockchain-based decentralized trust management in vehicular networks IEEE Internet Things J. 2018 6 2 1495-1505
[15]
Jiang T, Fang H, and Wang H Blockchain-based internet of vehicles: distributed network architecture and performance analysis IEEE Internet Things J. 2018 6 3 4640-4649
[16]
Kang J, Yu R, Huang X, Wu M, Maharjan S, Xie S, and Zhang Y Blockchain for secure and efficient data sharing in vehicular edge computing and networks IEEE Internet Things J. 2018 6 3 4660-4670
[17]
Luo B, Li X, Weng J, Guo J, and Ma J Blockchain enabled trust-based location privacy protection scheme in vanet IEEE Trans. Veh. Technol. 2019 69 2 2034-2048
[18]
Lu Y, Huang X, Zhang K, Maharjan S, and Zhang Y Blockchain empowered asynchronous federated learning for secure data sharing in internet of vehicles IEEE Trans. Veh. Technol. 2020 69 4 4298-4311
[19]
Gai K, Wu Y, Zhu L, Zhang Z, and Qiu M Differential privacy-based blockchain for industrial Internet-of-Things IEEE Trans. Ind. Inform. 2019 16 6 4156-4165
Index Terms
- RepBFL: Reputation Based Blockchain-Enabled Federated Learning Framework for Data Sharing in Internet of Vehicles
Index terms have been assigned to the content through auto-classification.
Recommendations
A Federated Learning-Based Blockchain-Assisted Anomaly Detection Scheme to Prevent Road Accidents in Internet of Vehicles
ICCA '22: Proceedings of the 2nd International Conference on Computing AdvancementsIn the modern era, the internet of vehicles (IoV) is being utilized in commercial applications and extensively explored in research. However, internal fault in IoV can cause accidents on the road. Moreover, privacy concerns can hamper the internal data ...
Blockchain based secure data sharing system for Internet of vehicles: A position paper
AbstractOne of the benefits of Internet of Vehicles (IoV) is improved traffic safety and efficiency, for example due to the capability to share vehicular messages in real-time. While most of the vehicular messages only need to be shared by nearby ...
Highlights- We present a method based on fair blind signatures and threshold secret sharing to realize conditional privacy in vehicles.
- Our scheme not only prevents the acceptance of a fake message, but also mitigates the throughput limitation of ...
Privacy enabled driver behavior analysis in heterogeneous IoV using federated learning
AbstractInternet of Vehicles (IoV) is a paradigm of ITS that incorporates automobiles, transportation, information sharing, and traffic infrastructure management with the aim to improve safety on roads. Federated learning enables the Internet ...
Comments
Information & Contributors
Information
Published In
Dec 2021
642 pages
ISBN:978-3-030-96771-0
DOI:10.1007/978-3-030-96772-7
© Springer Nature Switzerland AG 2022.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 17 December 2021
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 01 Jan 2025