Hierarchical Graph Federated Learning for Cross-Domain Intelligent Transportation Networks
TL;DR
Experiments demonstrate that HG-FL achieves superior performance compared to centralized, flat federated, and graph-only baselines, and highlight the framework’s scalability, robustness, and effectiveness in achieving cross-domain generalization and service-level assurance within privacy-preserving ITS environments.
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.