Connected and Automated Vehicles (CAVs) represent a rapidly growing technology in the automotive domain sector, offering promising solutions to address challenges such as traffic accidents, congestion, and pollution. By leveraging CAVs, we have the opportunity to create a transportation system that is safe, efficient, and environmentally sustainable. Machine learning-based methods are widely used in CAVs for crucial tasks like perception, planning, and control, where machine learning models in CAVs are solely trained with the local vehicle data, and the performance is not certain when exposed to new environments or unseen conditions. Federated learning (FL) is a decentralized machine learning approach that enables multiple vehicles to develop a collaborative model in a distributed learning framework. FL enables CAVs to learn from a broad range of driving environments and improve their overall performances while ensuring the privacy and security of local vehicle data. In this paper, we review the progress accomplished by researchers in applying FL to CAVs. A broader view of various data modalities and algorithms that have been implemented on CAVs is provided. Specific applications of FL are reviewed in detail, and an analysis of research challenges is presented.


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    Titel :

    A Survey of Federated Learning for Connected and Automated Vehicles


    Beteiligte:


    Erscheinungsdatum :

    24.09.2023


    Format / Umfang :

    563433 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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