Community　detection　is　a　trendy　area　in　research　on　complex　network　analysis　and　has　a　wide　range　of　real-world　applications,　like　advertising.　As　people　become　increasingly　concerned　about　privacy,　protecting　participants’　privacy　in　distributed　community　detection　has　become　a　new　challenge.　When　applied　to　not　identically　and　independently　distributed　(Non-IID)　data,　most　federated　graph　algorithms　suffer　from　the　weight　divergence　problem　caused　by　diversified　training　of　local　and　global　models,　resulting　in　accuracy　degradation.　Furthermore,　the　privacy　protection　approaches　based　on　anonymization,　such　as　differential　privacy　(DP),　and　cryptography,　such　as　homomorphic　encryption　(HE),　incur　accuracy　loss　and　high　time　consumption,　respectively.　In　this　paper,　we　propose　a　globally　consistent　vertical　federated　graph　autoencoder　(GCVFGAE)　algorithm,　which　builds　a　globally　consistent　model　among　the　coordinator　and　all　participants　to　solve　the　Non-IID　graph　data　problem.　As　well,　an　attribute　blinding　strategy　based　on　security　aggregation　is　developed　to　protect　the　network　privacy　of　each　participant　without　losing　accuracy.　Both　real-world　and　artificial　networks’　experiments　show　that　our　algorithm　reaches　higher　accuracy　than　the　existing　vertical　federated　graph　neural　networks　(GNNs)　and　the　simple　distributed　graph　autoencoder　without　federated　learning　and　detects　communities　identical　to　those　found　by　the　standard　graph　autoencoder　(GAE).　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.