As　an　important　part　of　Intelligent　Transportation　System　(ITS),　traffic　flow　prediction　has　attracted　more　and　more　attention　from　academia　and　industry.　However,　since　traffic　flow　data　are　distributed　among　different　organizations,　traditional　centralized　learning　methods　are　no　longer　applicable,　thus　federated　learning　has　begun　to　play　an　important　role　in　traffic　flow　prediction　tasks.　In　this　paper,　we　design　an　improved　traffic　flow　prediction　model　based　on　federated　learning.　At　the　same　time,　we　consider　the　interaction　of　topology　and　feature　space　between　traffic　data　owned　by　different　organizations,　and　propose　a　method　named　Federated　Neighbor　Aggregation　(FedNe).　The　server　update　the　model　parameters　with　both　the　physical　connectivity　and　similarity　relationships　between　the　organizations,　and　finally　generate　a　personalized　model　for　each　organization.　Experimental　results　on　real-world　dataset　show　that　our　proposed　method　has　the　lowest　average　MAE　and　average　RMSE.　Finally,　based　on　our　method,　when　new　clients　participate,　they　can　also　quickly　obtain　their　personalized　models.　©　2023　IEEE.