Graphs　widely　exist　in　real-world,　and　Graph　Neural　Networks　(GNNs)　have　exhibited　exceptional　efficacy　in　graph　learning　in　diverse　fields.　With　the　strengthening　of　data　privacy　protection　worldwide　in　recent　years,　Federated　graph　neural　networks　(FedGNNs)　have　gained　increasing　attention　in　academia　and　industry　owing　to　their　ability　to　train　the　model　in　a　collaborative　manner　while　complying　with　the　privacy　protection　regulations.　However,　in　federated　learning,　the　non-independent　and　identically　distributed　(non-IID)　problem　of　local　data　possessed　by　multiple　participants　can　significantly　undermine　model　accuracy.　We　propose　a　new　Decentralized　Federated　Graph　Normalized　AutoEncoder　(D-FGNAE).　First,　the　model　is　designed　as　a　decentralized　federated　learning　framework　with　dynamically　assigned　tripartite　roles.　This　design　eliminates　the　fixed　server　role　found　in　traditional　federated　learning,　enhances　system　fault　tolerance,　avoids　single　points　of　failure,　and　protects　model　privacy.　Second,　the　splitting　and　correcting　of　calculation　by　layer　in　the　model,　along　with　the　special　design　of　the　normalization　layer,　effectively　tackle　the　non-IID　problem　in　both　the　structural　and　attribute　aspects.　Experimental　results　on　real-world　networks　demonstrate　the　effectiveness　of　D-FGNAE,　which　can　achieve　nearly　the　same　accuracy　as　the　centralized　model.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.