Hierarchical Graph Federated Learning for Cross-Domain Intelligent Transportation Networks

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 real-world 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 privacy-preserving ITS environments.