Emerging Blockchain and Reputation Management in Federated Learning: Enhanced Security and Reliability for Internet of Vehicles (IoV)

Abstract

Artificial intelligence (AI) technologies have been applied to the Internet of Vehicles (IoV) to provide convenience services such as traffic flow prediction. However, concerns regarding privacy and security are on the rise as huge amounts of data are aggregated to form large models (LMs). Although federated learning (FL), which trains and updates a model without sharing the actual datasets, has been intensively researched to prevent privacy breaches, there are still potential security threats like a single point of failure and intentional tampering with malicious data. This is because of the vulnerability of a central curator and a lack of authentication. As participants, they (i.e., vehicles) may unintentionally update low-quality data caused by poor wireless connectivity, unstable availability, and insufficient training datasets. They may also intentionally update unreliable data to carry out poisoning attacks. The divergence among local models, trained on non-independent and identically distributed (non-IID) data, can slow convergence and diminish model accuracy when these models are aggregated. Therefore, it is important to carefully select trustworthy participants. In this paper, we propose a new reliable and secure federated learning for IoV based on decentralized blockchain and reputation management. To cope with a single point of failure, injection of malicious data, and lack of authentication while ensuring privacy and traceability, our scheme combines blockchain and a lightweight digital signature. Moreover, we employ the concept of the reputation of vehicles to select suitable participants with reliability, ultimately improving accuracy. Security analysis results, including comparisons with previous works, prove that the proposed scheme can address security concerns. The results of performance evaluations demonstrate the effectiveness of our proposed scheme