  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.
