Motivated　by　the　rapid　development　of　the　Internet　of　Vehicles　(IoV)　and　fifth-generation　mobile　communication　technology,　vehicle　clients　have　put　forward　higher　requirements　for　reducing　communication　overhead　and　enhancing　data　privacy.　The　recently　emerging　distributed　learning　method,　federated　learning　(FL),　can　efficiently　process　data　parameters　while　protecting　clients＇　privacy.　Therefore,　we　propose　an　FL-empowered　resource　allocation　and　communication　optimization　framework　in　the　IoV.　The　purpose　of　this　paper　is　to　minimize　the　loss　function　of　FL　while　ensuring　efficient　communication　between　vehicle　devices　and　the　BS.　Based　on　this　purpose,　the　problem　of　the　uplink　wireless　resource　allocation　and　parameter　quantization　in　the　IoV　is　formulated.　Then,　the　shortest　path　optimization　algorithm　is　used　to　achieve　wireless　resource　allocation　recursively.　Furthermore,　an　error-feedback　enabled　quantized　stochastic　gradient　descent　algorithm　is　adopted,　which　reduces　the　size　of　the　parameters　transmitted　to　the　BS　by　quantifying　the　local　parameters　of　the　vehicle　devices,　and　the　error-feedback　mechanism　is　used　to　speed　up　the　convergence　of　the　FL.　Finally,　simulation　results　show　that　the　proposed　scheme　outperforms　the　comparison　schemes　in　terms　of　global　loss　and　convergence　accuracy.　©　2022　IEEE.