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

Highlights: What are the main findings? From a review of 39 Intelligent Transportation System (ITS) studies, we identified 15 applications using federated learning (FL) frameworks, grouped into three architecture families (privacy-focused, pipeline-preserving, and advanced-infrastructure/resourcecoordinated). We then selected three representative frameworks for detailed comparison. Our qualitative, architecture-level comparative analysis indicates that hierarchical, edge-assisted FL deployments (with intermediate aggregation at roadside units (RSUs) or cloudlets) are conceptually better aligned with scalability, latency, and stability requirements in ITS deployments than client–server baselines. Digital Twin (DT) + Hierarchical Federated Learning (HFL) (DT+HFL) and Transfer Learning with Convolutional Neural Networks (TFL-CNN) emerged as complementaryreference frameworks, combining simulation-based hierarchy and practical edge-level coordination. We instantiated the adaptation methodology on four representative ITS applications—covering traffic prediction, real-time accident detection, transport mode identification, and driver profiling—to analyze their transition paths from centralizedmachine learning (ML) to FL-based deployments. The proposed federability criteria—built around three diagnostic questions on (i) how naturally data sources are distributed across vehicles, edge nodes, and infrastructure; (ii) the feasibility of executing the core processing at edge or roadside units; and (iii) the decomposability of the learning pipeline into node-level models plus an aggregation step—effectively identify ITS applications that can transition from centralized ML to distributed FL settings. What are the implications of the main findings? Edge-assisted FL architectures are conceptually well suited for vehicular and trafficdomains with intermittent connectivity and heterogeneous data. This study provides a structured pathway for migrating existing ITS applicationstoward federated privacy-preserving and scalable smart-city deployments. Intelligent Transportation Systems (ITS) have progressively incorporated machine learning to optimize traffic efficiency, enhance safety, and improve real-time decision-making. However, the traditional centralized machine learning (ML) paradigm faces critical limitations regarding data privacy, scalability, and single-point vulnerabilities. This study explores 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 federated learning (FL) frameworks: privacy-focused, integrable, and advanced infrastructure. Three representative frameworks—Federated Learning-based Gated Recurrent Unit (FedGRU), Digital Twin + Hierarchical Federated Learning (DT + HFL), and Transfer Learning with Convolutional Neural Networks (TFL-CNN)—were comparatively analyzed against a client–server baseline to assess their suitability for ITS adaptation. Our qualitative, architecture-level comparison suggests that DT + HFL and TFL-CNN, characterized by hierarchical aggregation and edge-level coordination, are conceptually better aligned with scalability and stability requirements in vehicular and traffic deployments than pure client–server baselines. FedGRU, while conceptually relevant as a meta-framework for coordinating multiple organizational models, is primarily intended as a complementary reference rather 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. This work does not introduce new experimental results; instead, it provides a qualitative, architecture-level comparison and adaptation guideline to support the migration of ITS applications toward federated learning. 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.