Hierarchical federated transfer learning in digital twin-based vehicular networks

Abstract

In recent research on the Digital Twin-based Vehicular Ad hoc Network (DT-VANET), Federated Learning (FL) has shown its ability to provide data privacy. However, Federated learning struggles to adequately train a global model when confronted with data heterogeneity and data sparsity among vehicles, which ensure suboptimal accuracy in making precise predictions for different vehicle types. To address these challenges, this paper combines Federated Transfer Learning (FTL) to conduct vehicle clustering related to types of vehicles and proposes a novel Hierarchical Federated Transfer Learning (HFTL). We construct a framework for DT-VANET, along with two algorithms designed for cloud server model updates and intra-cluster federated transfer learning, to improve the accuracy of the global model. In addition, we developed a data quality score-based mechanism to prevent the global model from being affected by malicious vehicles. Lastly, detailed experiments on real-world datasets are conducted, considering different performance metrics that verify the effectiveness and efficiency of our algorithm.

Keywords

Vehicular ad-hoc network / Hierarchical federated transfer learning / Vehicular digital twin / Autonomous vehicle / Digital twin-based vehicular networks

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work is partially supported by the National Science Foundation (2343619, 2416872, 2244219, and 2146497).

References

[1] M. Noor-A-Rahim, Z. Liu, H. Lee, G.M.N. Ali, D. Pesch, P. Xiao, A survey on resource allocation in vehicular networks, IEEE Trans. Intell. Transp. Syst. 23 (2) (2020) 701-721.

[2] Q. Zia, M.S. Farooq, A. Abid, Improving response time of vehicular ad hoc networks (vanet), 2016.

[3] C. He, T.H. Luan, R. Lu, Z. Su, M. Dong, Security and privacy in vehicular digital twin networks: Challenges and solutions, IEEE Wirel. Commun. (2022).

[4] L.U. Khan, E. Mustafa, J. Shuja, F. Rehman, K. Bilal, Z. Han, C.S. Hong, Federated learning for digital twin-based vehicular networks: Architecture and challenges, IEEE Wirel. Commun. (2023).

[5] S. AbdulRahman, H. Tout, H. Ould-Slimane, A. Mourad, C. Talhi, M. Guizani, A survey on federated learning: The journey from centralized to distributed on-site learning and beyond, IEEE Internet Things J. 8 (7) (2020) 5476-5497.

[6] B. Li, Y. Wu, J. Song, R. Lu, T. Li, L. Zhao, Deepfed: Federated deep learning for intrusion detection in industrial cyber-physical systems, IEEE Trans. Ind. Informatics 17 (8) (2020) 5615-5624.

[7] L.U. Khan, W. Saad, Z. Han, C.S. Hong, Dispersed federated learning: Vision, Taxon. Futur. Dir. IEEE Wirel. Commun. 28 (5) (2021) 192-198.

[8] J. Konečnỳ, H.B. McMahan, F.X. Yu, P. Richtárik, A.T. Suresh, D. Bacon, Federated learning: Strategies for improving communication efficiency, 2016, arXiv preprint arXiv:1610.05492.

[9] B. Li, Y. Jiang, Q. Pei, T. Li, L. Liu, R. Lu, Feel: Federated end-to-end learning with non-iid data for vehicular ad hoc networks, IEEE Trans. Intell. Transp. Syst. 23 (9) (2022) 16 728-16 740.

[10] S.S. Sepasgozar, S. Pierre, Fed-ntp: A federated learning algorithm for network traffic prediction in vanet, IEEE Access 10 (2022) 119 607-119 616.

[11] L. Zhang, B. Zhang, Hierarchical machine learning-a learning methodology inspired by human intelligence, in: International Conference on Rough Sets and Knowledge Technology, Springer, 2006, pp. 28-30.

[12] F. Gonçalves, J. Macedo, A. Santos, An intelligent hierarchical security framework for vanets, Information 12 (11) (2021) 455.

[13] M.S. HaghighiFard, S. Coleri, Hierarchical federated learning in multi-hop cluster-based vanets, 2024, arXiv preprint arXiv:2401.10361.

[14] Y. Liu, Y. Kang, C. Xing, T. Chen, Q. Yang, A secure federated transfer learning framework, IEEE Intell. Syst. 35 (4) (2020) 70-82.

[15] S. Otoum, N. Guizani, H. Mouftah, On the feasibility of split learning, transfer learning and federated learning for preserving security in its systems, IEEE Trans. Intell. Transp. Syst. (2022).

[16] Q. Zia, A survey of data-centric protocols for wireless sensor networks, Comput. Sci. Syst. Biology, OMICS Publ. Group 8 (3) (2015) 127-131.

[17] S.-S. Wang, Y.-S. Lin, Passcar: A passive clustering aided routing protocol for vehicular ad hoc networks, Comput. Commun. 36 (2) (2013) 170-179.

[18] S.A. Rashid, L. Audah, M.M. Hamdi, S. Alani, Prediction based efficient multi-hop clustering approach with adaptive relay node selection for vanet, J. Commun. 15 (4) (2020) 332-344.

[19] A. Temurnikar, P. Verma, G. Dhiman, A pso enable multi-hop clustering algorithm for vanet, Int. J. Swarm Intell. Res. (IJSIR) 13 (2) (2022) 1-14.

[20] Q. Zia, C. Wang, S. Zhu, Y. Li, Priority based inter-twin communication in vehicular digital twin networks, Int. J. Parallel Emergent Distrib. Syst. (2024) 1-16.

[21] Y. Dai, Y. Zhang, Adaptive digital twin for vehicular edge computing and networks, J. Commun. Inf. Networks 7 (1) (2022) 48-59.

[22] L.U. Khan, W. Saad, D. Niyato, Z. Han, C.S. Hong, Digital-twin-enabled 6 g: Vision, architectural trends, and future directions, IEEE Commun. Mag. 60 (1) (2022) 74-80.

[23] L.U. Khan, Z. Han, W. Saad, E. Hossain, M. Guizani, C.S. Hong, Digital twin of wireless systems: Overview, taxonomy, challenges, and opportunities, IEEE Commun. Surv. & Tutorials 24 (4) (2022) 2230-2254.

[24] G. Colab, 2022, [Online] Available: https://colab.research.google.com/.