Federated　learning　can　effectively　protect　local　data　privacy　in　5G-V2X　environment　and　ensure　data　protection　in　Internet　of　vehicles　environment.　The　advantages　of　low　delay　of　5G　network　should　be　better　utilized　in　the　vehicle-road　cooperative　system.　But　the　existing　asynchronous　federated　learning　obtains　a　local　model　through　different　node　training　and　completes　the　update　of　the　global　model　through　the　central　server.　There　are　problems　such　as　single　point　of　failure,　privacy　leakage,　and　deviation　of　aggregation　parameters.　In　response　to　the　above　problems,　we　proposed　a　5G-V2X-oriented　asynchronous　federated　learning　privacy-preserving　computing　model　(AFLPC).　We　used　an　adaptive　differential　privacy　mechanism　to　reduce　noise　while　protecting　data　privacy.　A　weight-based　asynchronous　federated　learning　aggregation　update　method　is　proposed　to　reasonably　control　the　proportion　of　parameters　submitted　by　users　with　different　training　speeds　in　the　aggregation　parameters　and　actively　update　the　aggregation　parameters　of　lagging　users,　so　as　to　effectively　reduce　the　negative　impact　on　the　model　caused　by　the　different　speed　of　finding　you.　Experiments　show　that　the　proposed　method　can　effectively　ensure　the　credibility　and　privacy　of　asynchronous　federated　learning　in　5G-V2X　scenarios　and　at　the　same　time　improve　the　utility　of　the　model.