Effective and Efficient Cross-City Traffic Knowledge Transfer: A Privacy-Preserving Perspective
Zhihao Zeng, Ziquan Fang, Yuting Huang, Lu Chen, Yunjun Gao
TL;DR
This work tackles privacy-preserving cross-city traffic prediction under data scarcity by introducing FedTT, a framework that avoids raw data sharing through a transformed-data approach. It combines four modules—Traffic View Imputation for data quality, Traffic Domain Adapter for domain alignment, Traffic Secret Transmission for privacy-preserving aggregation, and Federated Parallel Training for efficiency—to enable knowledge transfer from data-rich source cities to a data-scarce target city. Extensive experiments on four real-world datasets show FedTT consistently surpasses 14 baselines across multiple prediction tasks and significantly reduces communication and training time. The proposed approach offers a practical pathway to robust, scalable, privacy-protecting cross-city TP in real-world urban settings.
Abstract
Traffic prediction targets forecasting future traffic conditions using historical traffic data, serving a critical role in urban computing and transportation management. To mitigate the scarcity of traffic data while maintaining data privacy, numerous Federated Traffic Knowledge Transfer (FTT) approaches have been developed, which use transfer learning and federated learning to transfer traffic knowledge from data-rich cities to data-scarce cities, enhancing traffic prediction capabilities for the latter. However, current FTT approaches face challenges such as privacy leakage, cross-city data distribution discrepancies, low data quality, and inefficient knowledge transfer, limiting their privacy protection, effectiveness, robustness, and efficiency in real-world applications. To this end, we propose FedTT, an effective, efficient, and privacy-aware cross-city traffic knowledge transfer framework that transforms the traffic data domain from the data-rich cities and trains traffic models using the transformed data for the data-scarce cities. First, to safeguard data privacy, we propose a traffic secret transmission method that securely transmits and aggregates traffic domain-transformed data from source cities using a lightweight secret aggregation approach. Second, to mitigate the impact of traffic data distribution discrepancies on model performance, we introduce a traffic domain adapter to uniformly transform traffic data from the source cities' domains to that of the target city. Third, to improve traffic data quality, we design a traffic view imputation method to fill in and predict missing traffic data. Finally, to enhance transfer efficiency, FedTT is equipped with a federated parallel training method that enables the simultaneous training of multiple modules. Extensive experiments using 4 real-life datasets demonstrate that FedTT outperforms the 14 state-of-the-art baselines.