Federated Learning Frameworks for Intelligent Transportation Systems: A Comparative Adaptation Analysis

Intelligent Transportation Systems (ITS) have progressively incorporated machine learning (ML) to optimize traffic efficiency, enhance safety, and improve real-time decision-making. However, the traditional centralized ML paradigm faces critical limitations regarding data privacy, scalability, and single-point vulnerabilities. This study explores federated learning (FL) as a decentralized alternative that preserves privacy by training local models without transferring raw data. Based on a systematic literature review encompassing 39 ITS-related studies, this work classifies applications according to their architectural detail—distinguishing systems from models—and identifies three families of FL frameworks: privacy-focused, integrable, and advanced-infrastructure. Three representative frameworks—FedGRU, DT+HFL, and TFL-CNN—were comparatively analyzed against a client–server baseline to assess their suitability for ITS adaptation. The findings reveal that DT+HFL and TFL-CNN, characterized by hierarchical aggregation and intermediate edge components such as cloudlets or roadside units (RSUs), offer superior scalability and adaptability across vehicular and traffic domains. FedGRU, while conceptually relevant as a meta-framework for coordinating multiple organizational models, is more suitable as a complementary reference than as a standalone architecture for large-scale ITS deployment. Through application-level evaluations—including traffic prediction, accident detection, transport-mode identification, and driver profiling—this study demonstrates that FL can be effectively integrated into ITS with moderate architectural adjustments. Overall, the results establish a solid methodological foundation for migrating centralized ITS architectures toward federated, privacy-preserving intelligence, in alignment with the evolution of edge and 6G infrastructures.