@article{scholars19774,
            year = {2024},
           pages = {156--162},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {IEEE Wireless Communications},
             doi = {10.1109/MWC.012.2200373},
          number = {2},
            note = {cited By 12},
          volume = {31},
           title = {Federated Learning for Digital Twin-Based Vehicular Networks: Architecture and Challenges},
          author = {Khan, L. U. and Mustafa, E. and Shuja, J. and Rehman, F. and Bilal, K. and Han, Z. and Hong, C. S.},
            issn = {15361284},
        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 twin-based 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 article is concluded and an outlook on open challenges is presented. {\^A}{\copyright} 2002-2012 IEEE.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149825469&doi=10.1109\%2fMWC.012.2200373&partnerID=40&md5=ba2dce1512c3cb1e95bd1d591c8b96eb},
        keywords = {Learning systems; Network architecture; Privacy-preserving techniques, Computational modelling; Edge server; Network applications; Optimization models; Performance optimizations; Physical systems; Privacy preserving; Vehicular networks; Virtual models; Wireless communications, E-learning}
}