Heterogeneous-Aware Traffic Prediction: A Privacy-Preserving Federated Learning Framework

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Zhihao Zeng, Ziquan Fang, Yuting Huang, Qilong Wang, Lu Chen, Yunjun Gao

Published: 01 Jan 2025, Last Modified: 22 Sept 2025ICDE 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Spatio-temporal traffic series prediction is essential in intelligent transportation systems, benefiting various applications such as route planning, vehicle dispatching, and congestion prediction. To tackle privacy leakage aroused by centralized forecasting methods, Federated Learning (FL), a privacy-preserving approach for decentralized model training into disjointed federated clients, has garnered widespread adoption in numerous traffic prediction endeavors. However, existing FL-based approaches ignore spatio-temporal heterogeneity among federated clients, including spatial feature skew, temporal coverage skew, and data quality skew. This makes them inapplicable and unsuitable to real-world scenarios and exhibits subpar prediction performance. To this end, we perform the first study of heterogeneous-aware traffic prediction in the federated environment, proposing a unified and effective framework named Fed4TP. It offers general federated capability for various centralized forecast models, supporting flow, speed, and occupancy prediction tasks. To address spatial feature heterogeneity, Fed4TP develops multi-dimensional personalized federated learning with positive samples contrastive learning for clustering to achieve personalized aggregation and global sharing across diverse clients. To overcome temporal coverage heterogeneity, Fed4TP designs a time window-based federated training mechanism, sequentially training client models and learning missed traffic information with varying time coverage. To tackle data quality heterogeneity, Fed4TP introduces a dual-driven method, i.e., global detection and local denoising, to improve client data quality. Extensive experiments on 4 real-life datasets verify the effectiveness and scalability superiority of Fed4TP in various federated-based traffic prediction tasks, compared with 24 well-known and state-of-the-art baselines. The source code and data of this work are available at https://github.com/ZJU-DAILY/Fed4TP.