Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach

作者：Yi Liu, James J. Q. Yu, Jiawen Kang, Dusit Niyato, Shuyu Zhang · 发表于：IEEE Internet of Things Journal · 年份：2020 · DOI：10.1109/jiot.2020.2991401 · 被引用次数：707 · 研究领域：Traffic Prediction and Management Techniques、Privacy-Preserving Technologies in Data、Vehicular Ad Hoc Networks (VANETs)

Existing traffic flow forecasting approaches by deep learning models achieve excellent success based on a large volume of data sets gathered by governments and organizations. However, these data sets may contain lots of user's private data, which is challenging the current prediction approaches as user privacy is calling for the public concern in recent years. Therefore, how to develop accurate traffic prediction while preserving privacy is a significant problem to be solved, and there is a tradeoff between these two objectives. To address this challenge, we introduce a privacy-preserving machine learning technique named federated learning (FL) and propose an FL-based gated recurrent unit neural network algorithm (FedGRU) for traffic flow prediction (TFP). FedGRU differs from current centralized learning methods and updates universal learning models through a secure parameter aggregation mechanism rather than directly sharing raw data among organizations. In the secure parameter aggregation mechanism, we adopt a federated averaging algorithm to reduce the communication overhead during the model parameter transmission process. Furthermore, we design a joint announcement protocol to improve the scalability of FedGRU. We also propose an ensemble clustering-based scheme for TFP by grouping the organizations into clusters before applying the FedGRU algorithm. Extensive case studies on a real-world data set demonstrate that FedGRU can produce predictions that are merely 0.76 km/h w...