FedTraffic: A Hierarchical Federated Learning Framework for Traffic Flow Prediction in Intelligent Transportation Systems

FedTP: A Federated Learning Framework for Traffic Prediction

FedTP is proposed, a federated learning framework that integrates gradient conflict elimination into the aggregation process and harmonizes local updates, thereby improving fairness across clients without compromising overall predictive accuracy.

Traffic congestion management using attention based federated hybrid deep learning model in Vehicle-to-Everything (V2X) systems

A hybrid deep learning framework that integrates Bidirectional Long Short-Term Memory and Gated Recurrent Unit networks with an attention mechanism within a federated learning paradigm is proposed, which enables decentralized model training across multiple data sources without requiring raw data sharing, thereby preserving privacy while maintaining predictive performance.

Federated Learning Approach for Multi-Regional Traffic Flow Prediction

Experiments on controlled synthetic data and the Q-Traffic real-world dataset demonstrate that the proposed framework improves predictive accuracy, cross-client stability, and robustness under heterogeneous federated traffic scenarios.

Hierarchical Graph Federated Learning for Cross-Domain Intelligent Transportation Networks

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.

Lightweight Aggregation Protocols for Green Federated Learning in Vehicle-to-Cloud Systems

Online Federated Learning on Resource-Limited IoT Devices for Traffic Flow Prediction in Smart Mobility Ecosystems

Traffic congestion significantly impacts safety and urban livability in smart cities, motivating the development of accurate Traffic Flow Prediction (TFP) systems. Traditional deep learning approaches typically rely on centralized training, which is difficult to scale in distributed Internet of Things (IoT) environments. To address these limitations, decentralized paradigms such as Local Learning (LL) and Federated Learning (FL) enable on-device training and collaborative model updates while preserving data locality. For real-time TFP, the inherently non-stationary nature of traffic data necessitates continuous model adaptation, making online federated learning essential for scalable and collaborative deployment. However, this setting remains challenging because traffic data are typically non-IID across clients, with local patterns varying significantly across locations and devices. This paper presents an exploratory study of online federated learning for TFP on resource-constrained IoT devices. Using a GRU-based network as a common backbone, the Online Federated Learning paradigm is benchmarked relative to Centralized and Local Learning as reference baselines. A performance evaluation was conducted by evaluating RMSE and MAE on the PEMS-BAY dataset. Robustness to non-IID data is further assessed using FedProx and SCAFFOLD. Results show that LL achieves the lowest prediction error, whereas FL degrades as the number of local epochs increases, and non-IID mitigation strategies provide limited improvements under low-latency constraints. Overall, online federated learning is a viable approach for real-time TFP, but its performance is highly sensitive to client heterogeneity.