Federated Learning for Privacy-Preserving Vehicle Trajectory Prediction in Intelligent Transportation Networks
Keywords:
federated learning; vehicle trajectory prediction; intelligent transportation systems; privacy; edge computing; governance; robustness; fairness; sustainability; data policyAbstract
Vehicle trajectory prediction has become a foundational capability for intelligent transportation networks, supporting collision avoidance, cooperative routing, signal optimization, and mixed-autonomy traffic management. Conventional centralized prediction models require large-scale movement data to be aggregated in cloud repositories, creating substantial privacy, cybersecurity, and governance risks because mobility traces can reveal home locations, work patterns, health visits, social ties, and other sensitive inferences. Federated learning offers a distributed alternative in which vehicles, roadside units, and edge servers collaboratively train prediction models without transmitting raw trajectories to a central authority. This paper examines federated learning for privacy-preserving vehicle trajectory prediction from a system-level perspective. It argues that privacy protection cannot be reduced to a single technical mechanism, but must be understood as an architectural and institutional property emerging from the interaction of secure aggregation, differential privacy, client sampling, communication scheduling, edge computing, and legal accountability. The paper analyzes structural trade-offs involving prediction accuracy, personalization, communication cost, latency, energy use, robustness, fairness, and sustainability. It further considers governance challenges in intelligent transportation ecosystems, including consent, purpose limitation, auditability, liability, and cross-jurisdictional data policy. Through conceptual analysis, cross-domain comparisons, and forward-looking deployment scenarios, the paper shows that federated trajectory prediction is not merely a privacy-enhancing technique but a socio-technical infrastructure requiring coordinated design across models, networks, institutions, and public policy. The conclusion outlines research and policy priorities for deploying federated prediction systems that are accurate, equitable, resilient, and democratically accountable.
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