Mobile　Edge　Computing　(MEC)　distributes　resources　such　as　computing,　storage,　and　bandwidth　to　the　side　close　to　users,　which　can　provide　low-latency　services　to　in-vehicle　users,　thus　promising　a　more　efficient　and　safer　driving　environment.　However,　due　to　the　dynamic　scale　of　vehicle　and　the　variability　of　resource　requirements,　it　is　a　significant　challenge　to　quickly　obtain　effective　task　offloading　in　large-scale　vehicle　scenarios.　The　existing　studies　generally　adopt　the　centralized　decision-making　method,　with　long　decision-making　time　and　high　computational　overhead,　which　cannot　effectively　achieve　good　offloading　decisions　in　large-scale　scenarios.　To　address　these　problems,　we　propose　a　Multi-agent　Collaborative　Method　for　vehicular　task　offloading　using　Federated　Deep　Reinforcement　Learning　called　MCM-FDRL.　First,　each　vehicle　as　an　agent,　independently　makes　offloading　decisions　based　on　local　information.　Next,　the　offloading　decision　model　of　each　vehicle　is　obtained　through　federated　reinforcement　learning　training.　At　runtime,　an　effective　vehicle　offloading　plan　can　be　gradually　developed　through　multi-agent　collaboration.　Using　two　real-world　datasets,　experiments　show　that　the　MCM-FDRL　has　good　adaptability　and　scalability.　Moreover,　compared　to　the　state-of-the-art　methods,　the　task＇s　average　response　time　of　the　MCM-FDRL　is　reduced　by　9.75%-64.90%,　respectively.　　©　2002-2012　IEEE.