  The number of internet-connected devices has been exponentially growing with
the massive volume of heterogeneous data generated from various devices,
resulting in a highly intertwined cyber-physical system. Currently, the Edge
Intelligence System (EIS) concept that leverages the merits of edge computing
and Artificial Intelligence (AI) is utilized to provide smart cloud services
with powerful computational processing and reduce decision-making delays. Thus,
EIS offers a possible solution to realizing future Intelligent Transportation
Systems (ITS), especially in a vehicular network framework. However, since the
central aggregator server supervises the entire system orchestration, the
existing EIS framework faces several challenges and is still potentially
susceptible to numerous malicious attacks. Hence, to solve the issues mentioned
earlier, this paper presents the notion of secure edge intelligence, merging
the benefits of Federated Learning (FL), blockchain, and Local Differential
Privacy (LDP). The blockchain-assisted FL approach efficiently improves traffic
prediction accuracy and enhances user privacy and security by recording
transactions in immutable distributed ledger networks and providing a
decentralized reward mechanism system. Furthermore, LDP is empowered to
strengthen the confidentiality of data sharing transactions, especially in
protecting users' private data from various attacks. The proposed framework has
been implemented in two scenarios, i.e., blockchain-based FL to efficiently
develop the decentralized traffic management for vehicular networks and
LDP-based FL to produce randomized privacy protection using the IBM Library for
differential privacy.
