  Emerging intelligent transportation applications, such as accident reporting,
lane change assistance, collision avoidance, and infotainment, will be based on
diverse requirements (e.g., latency, reliability, quality of physical
experience). To fulfill such requirements, there is a significant need to
deploy a digital twin-based intelligent transportation system. Although the
twin-based implementation of vehicular networks can offer performance
optimization. Modeling twins is a significantly challenging task. Machine
learning (ML) can be a preferable solution to model such a virtual model, and
specifically federated learning (FL) is a distributed learning scheme that can
better preserve privacy compared to centralized ML. Although FL can offer
performance enhancement, it requires careful design. Therefore, in this
article, we present an overview of FL for the twin-based vehicular network. A
general architecture showing FL for the twin-based vehicular network is
proposed. Our proposed architecture consists of two spaces, such as twin space
and a physical space. The 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.
