Abstract:In the vehicular network, federated learning is an emerging paradigm to train deep learning models safely. However, the non‐identically independent distributed data collected by intelligent connected vehicles, expensive training overhead, and the existence of malicious participants significantly affect the efficiency of training and constrain the development of federated learning in the vehicular network. To address the above issues, this paper proposes a participant selection‐based asynchronous federated lear… Show more
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