Privacy-preserving blockchain-based federated learning for traffic flow prediction

Abstract

As accurate and timely traffic flow information is extremely important for traffic management, traffic flow prediction has become a vital component of intelligent transportation systems. However, existing traffic flow prediction methods based on centralized machine learning need to gather raw data for model training, which involves serious privacy exposure risks. To address these problems, federated learning that shares model updates without exchanging raw data, has recently been introduced as an efficient solution for achieving privacy protection. However, the existing federated learning frameworks are based on a centralized model coordinator that still suffers from severe security challenges, such as a single point of failure. Thereby, a consortium blockchain-based federated learning framework is proposed to enable decentralized, reliable, and secure federated learning without a centralized model coordinator. In the proposed framework, the model updates from distributed vehicles are verified by miners to prevent unreliable model updates and are then stored on the blockchain. In addition, to further protect model privacy on the blockchain, a differential privacy method with a noise-adding mechanism is applied for the blockchain-based federated learning framework. Numerical results illustrate that the proposed schemes can effectively prevent data poisoning attacks and improve the privacy protection of model updates for secure and privacy-preserving traffic flow prediction.