Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· pp. 6409-6420· 0 citations· 34 references
TL;DR
This work proposes FedTT, a federated spatio-temporal learning framework for cross-city traffic knowledge transfer that consistently outperforms 18 state-of-the-art baselines, achieving improved prediction accuracy while maintaining strong empirical resistance to attacks.
Abstract
Traffic prediction aims to forecast future traffic conditions from historical spatio-temporal data. In practice, many cities face limited traffic data due to incomplete sensing infrastructures, making accurate prediction difficult. Federated traffic knowledge transfer offers a promising solution by allowing data-rich source cities to assist data-scarce target cities while keeping local data private. However, existing methods still face key challenges, including cross-city distribution discrepancies, incomplete or noisy observations, and potential privacy leakage from shared gradients or model parameters during federated optimization. To this end, we propose FedTT, a federated spatio-temporal learning framework for cross-city traffic knowledge transfer. FedTT features three co-designed components: (i) a Traffic Domain Adapter (TDA) that explicitly aligns heterogeneous traffic data distributions across cities to improve transfer effectiveness, (ii) a Traffic View Imputation (TVI) module that enhances data quality by completing missing traffic observations through spatio-temporal dependency modeling, and (iii) a lightweight Traffic Secret Aggregation (TSA) protocol that enables attack-resistant knowledge aggregation with formal privacy analysis, without relying on heavy cryptographic primitives. Extensive experiments on four real-world traffic datasets demonstrate that FedTT consistently outperforms 18 state-of-the-art baselines, achieving improved prediction accuracy while maintaining strong empirical resistance to attacks.
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.
Zhi-Cheng Wang, Tao Zhang, Yi-Meng Zhu et al.· Applied Sciences· 0 citations
FedTraffic, a hierarchical federated learning framework for traffic flow forecasting that integrates Edge–Fog–Cloud computing, hybrid deep learning, adaptive federated optimization, and Explainable Artificial Intelligence, is proposed.
Meta-FedGeo is introduced, a federated learning framework that integrates meta-learning and spatiotemporal transformers to address key challenges in urban GeoAI for smart cities and advances GeoAI toward scalable, adaptive, and practical applications in smart city environments.
R. Jean, Stabak Roy· ISPRS International Journal...· 0 citations
FedSTAR is proposed, a privacy-preserving cross-border recommendation framework that integrates spatio-temporal dynamic modeling with federated graph neural networks and delivers both high accuracy and strong robustness, offering a secure and practically viable solution for cross-border recommendation.
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.
M. Pizzolante, A. Shumba, T. Montanaro et al.· Annual International Compute...· 0 citations
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.
Baobao Chai, Zhongyuan Yu, Tianqing He et al.· 0 citations