A Fair and Trustworthy Hierarchical Federated Learning Scheme for Digital Twins in the Internet of Vehicles
Q H Fan, Yang Xin, Bin Jia, Xinze Zhang · IEEE Internet of Things Journal · 2024
Digital twins (DTs) support real time analysis and provide a reliable simulation platform for the Internet of Vehicles (IoV). DT modeling relies on a large amount of data, based on their own safety considerations, most clients are not willing to provide relevant data. Due to the characteristics of distributed collaboration and privacy protection, federated learning (FL) is a promising technique for DT modeling. The combination of the above two technologies can greatly accelerate the development of the IoV. However, an important challenge is the unbalanced distribution of local data across clients, which leads to client drift and affects the performance of the global model. Malicious clients uploading false parameters or low-quality models will also cause unsatisfactory model accuracy. In addition, trust between clients is not established in advance, and hidden safety issues arise when clients perform cooperative training with each other. Therefore, in this article, we propose a hierarchical FL (HFL) scheme for performing DT modeling tasks. To solve the problems of performance degradation and the divergence of FL models caused by nonindependent and identical distribution (Non-IID) data, we design a regularization algorithm and a data enhancement algorithm from the client-side and edge server-side, respectively. In addition, we design an auditing method to defend against attacks from malicious clients. To build trust between clients, we present a reputation management model that can effectively prevent swing attacks. The numerical results show that our proposed scheme not only outperforms the baseline methods in terms of model accuracy and attack resistance, but also filters out malicious clients to perform fair and efficient reputation computations.