Traffic　flow　is　considered　as　a　critical　feature　of　intelligent　transportation　systems　(ITS).　Accurately　forecasting　future　vehicular　volumes　is　an　effective　means　of　mitigating　traffic　congestion.　However,　the　nonlinear　and　complex　traffic　flow　characteristics　make　the　traditional　approaches　unable　to　achieve　satisfactory　prediction　performance.　Although　existing　methods　based　on　deep　learning　models　have　improved　the　accuracy　of　traffic　flow　prediction,　the　spatio-temporal　features　of　traffic　flow　data　are　still　not　fully　explored.　Moreover,　existing　methods　pay　little　attention　to　the　task　of　training　models　in　a　decentralized　environment　where　data　are　distributed　across　multiple　clients.　To　solve　the　problems　mentioned　above,　we　propose　a　novel　network　model　called　Graph　Transformer　Attention　Network　(GTAN)　for　traffic　flow　prediction,　which　can　effectively　extract　traffic　flow's　temporal　and　spatial　characteristics　by　considering　all　node　locations'　information　in　the　traffic　networks.　Then,　we　propose　a　training　strategy　called　Graph　Federated　Meta-learning　(FedGM),　solving　the　problem　of　topological　heterogeneity　by　combining　meta-learning　and　federated　learning,　to　achieve　an　optimal　initialization　model　which　can　quickly　adapt　to　different　traffic　networks　under　low　communication　cost.　Finally,　the　experimental　results　on　a　real　data　set　show　that　the　GTAN　model　has　better　prediction　performance　and　faster　meta-training　speed.　The　model　trained　by　FedGM　can　quickly　adapt　to　different　graph-structured　data　and　achieve　high　accuracy.　IEEE