Federated Learning for Digital Twin-Based Vehicular Networks: Architecture and Challenges

Abstract

A digital twin uses a virtual model of the physical system to fulfill the diverse requirements (e.g., latency, reliability, quality of physical experience) for emerging vehicular network applications. Although a twin-based implementation of vehicular networks can offer performance optimization, modeling a digital twin is a significantly challenging task. Federated learning (FL) is a better privacy-preserving, distributed learning scheme that can be used to model twin models. Although FL can offer performance enhancement, it requires careful design. Therefore, in this article, we present an overview of FL for a twinbased vehicular network. A general architecture for FL-enabled digital twins for a vehicular network is presented. Our proposed architecture consists of two spaces, such as twin space and the physical space. A physical space consists of all the physical entities (e.g., cars and edge servers) required for vehicular networks, whereas, the twin space refers to the logical space that is used for the deployment of twins. A twin space can be implemented either using edge servers and cloud servers. We also outline a few use cases of FL for the twin-based vehicular network. Finally, the paper is concluded and an outlook on open challenges is presented.