With　the　proliferation　of　smart　devices　and　the　Internet　of　Vehicles　(IoV)　technologies,　intelligent　fatigue　detection　has　become　one　of　the　most-used　methods　in　our　daily　driving.　Data　sharing　among　vehicles　can　be　used　to　optimize　fatigue　detection　models　and　ensure　driving　safety.　However,　data　privacy　issues　hinder　the　sharing　process.　Besides,　due　to　the　limitation　of　communication　and　computing　resources,　it　is　difficult　to　carry　out　training　and　data　transmission　on　vehicles.　To　tackle　these　challenges,　we　propose　FedSup,　a　communication-efficient　federated　learning　method　for　fatigue　driving　behaviors　supervision.　Inspired　by　the　resources　allocation　mechanism　in　edge　intelligence,　FedSup　dynamically　optimizes　the　sharing　model　with　tailored　client–edge–cloud　architecture　and　reduces　communication　overhead　by　a　Bayesian　Convolutional　Neural　Network　(BCNN)　data　selection　strategy.　To　improve　the　sharing　model　optimize　efficiency,　we　further　propose　an　asynchronous　parameters　aggregation　algorithm　to　automatically　adjust　the　mixing　weight　of　each　edge　model　parameter.　Extensive　experiments　demonstrate　that　the　FedSup　method　is　suitable　for　IoV　scenarios　and　outperforms　related　federated　learning　methods　in　terms　of　communication　overhead　and　model　accuracy.　©　2022　Elsevier　B.V.