Graph　neural　networks　(GNNs)　have　been　applied　successfully　in　many　machine　learning　tasks　due　to　their　advantages　in　utilizing　neighboring　information.　Recently,　with　the　global　enactment　of　privacy　protection　regulations,　federated　GNNs　have　gained　increasing　attention　in　academia　and　industry.　However,　the　graphs　owned　by　different　participants　could　be　non-independently-and-identically　distributed　(non-IID),　leading　to　the　deterioration　of　federated　GNNs＇　accuracy.　In　this　paper,　we　propose　a　globally　consistent　federated　graph　autoencoder　(GCFGAE)　to　overcome　the　non-IID　problem　in　unsupervised　federated　graph　learning　via　three　innovations.　First,　by　integrating　federated　learning　with　split　learning,　we　train　a　unique　global　model　instead　of　FedAvg-styled　global　and　local　models,　yielding　results　consistent　with　that　of　the　centralized　GAE.　Second,　we　design　a　collaborative　computation　mechanism　considering　overlapping　vertices　to　reduce　communication　overhead　during　forward　propagation.　Third,　we　develop　a　layer-wise　and　block-wise　gradient　computation　strategy　to　reduce　the　space　and　communication　complexity　during　backward　propagation.　Experiments　on　real-world　datasets　demonstrate　that　GCFGAE　achieves　not　only　higher　accuracy　but　also　around　500　times　lower　communication　overhead　and　1000　times　smaller　space　over-head　than　existing　federated　GNN　models.