B^2SFL: A Bi-level Blockchained Architecture for Secure Federated Learning-based Traffic Prediction
This addresses security and privacy risks in federated learning for traffic prediction applications, though it appears incremental as it combines existing blockchain and encryption techniques.
The paper tackles security vulnerabilities in federated learning for traffic prediction by proposing a bi-level blockchain architecture with distributed homomorphic-encrypted federated averaging, achieving functional secure and decentralized federated learning for real-world traffic prediction tasks.
Federated Learning (FL) is a privacy-preserving machine learning (ML) technology that enables collaborative training and learning of a global ML model based on aggregating distributed local model updates. However, security and privacy guarantees could be compromised due to malicious participants and the centralized FL server. This article proposed a bi-level blockchained architecture for secure federated learning-based traffic prediction. The bottom and top layer blockchain store the local model and global aggregated parameters accordingly, and the distributed homomorphic-encrypted federated averaging (DHFA) scheme addresses the secure computation problems. We propose the partial private key distribution protocol and a partially homomorphic encryption/decryption scheme to achieve the distributed privacy-preserving federated averaging model. We conduct extensive experiments to measure the running time of DHFA operations, quantify the read and write performance of the blockchain network, and elucidate the impacts of varying regional group sizes and model complexities on the resulting prediction accuracy for the online traffic flow prediction task. The results indicate that the proposed system can facilitate secure and decentralized federated learning for real-world traffic prediction tasks.