Citation

Javeed, Mohammed Saad and Maua, Jannatul and Ullah, Md Shafiq and Mridha, M. F. and Hossen, Md Jakir (2026) Hierarchical Graph Federated Learning for Cross-Domain Intelligent Transportation Networks. IEEE Open Journal of Intelligent Transportation Systems, 7. pp. 1904-1918. ISSN 2687-7813

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Abstract

Intelligent transportation systems (ITS) generate vast heterogeneous data from roadside units (RSUs), traffic management centers (TMCs), and vehicular networks, posing challenges for privacy, scalability, and cross-domain learning. This paper proposes a Hierarchical Graph Federated Learning (HG-FL) framework that integrates multi-level aggregation, spatio-temporal graph modeling, and autoencoder-based feature compression to enable privacy-preserving, distributed ITS analytics. The framework mirrors realworld ITS hierarchy through three tiers: local RSU-level training, regional TMC-level aggregation, and global cross-domain coordination. Experiments conducted on the CIC-IoV 2024 Decimal Dataset and the NF-ToN-IoT-v2 dataset demonstrate that HG-FL achieves superior performance compared to centralized, flat federated, and graph-only baselines. Specifically, on the combined dataset, HG-FL attains an AUROC of 0.964, AUPRC of 0.947, and F1-score of 0.929, while reducing communication cost to 162 MB and convergence rounds to 15. For SLA violation risk prediction, it achieves an RMSE of 0.121 and a Brier score of 0.059, outperforming baseline methods. These results highlight the framework’s scalability, robustness, and effectiveness in achieving cross-domain generalization and service-level assurance within privacypreserving ITS environments.

Item Type:	Article
Uncontrolled Keywords:	Federated learning, intelligent transportation systems
Subjects:	T Technology > TL Motor vehicles. Aeronautics. Astronautics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	04 Aug 2026 07:19
Last Modified:	04 Aug 2026 07:19
URII:	http://shdl.mmu.edu.my/id/eprint/16504

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