Graph　anomaly　detection　(GAD)　aims　to　identify　anomalous　graphs　that　significantly　deviate　from　other　ones,　which　has　raised　growing　attention　due　to　the　broad　existence　and　complexity　of　graph-structured　data　in　many　real-world　scenarios.　However,　existing　GAD　methods　usually　execute　with　centralized　training,　which　may　lead　to　privacy　leakage　risk　in　some　sensitive　cases,　thereby　impeding　collaboration　among　organizations　seeking　to　collectively　develop　robust　GAD　models.　Although　federated　learning　offers　a　promising　solution,　the　prevalent　non-IID　problems　and　high　communication　costs　present　significant　challenges,　particularly　pronounced　in　collaborations　with　graph　data　distributed　among　different　participants.　To　tackle　these　challenges,　we　propose　an　effective　federated　graph　anomaly　detection　framework　(FGAD).　We　first　introduce　an　anomaly　generator　to　perturb　the　normal　graphs　to　be　anomalous　and　train　a　powerful　anomaly　detector　by　distinguishing　generated　anomalous　graphs　from　normal　ones.　We　subsequently　leverage　a　student　model　to　distill　knowledge　from　the　trained　anomaly　detector　(teacher　model),　which　aims　to　maintain　the　personality　of　local　models　and　alleviate　the　adverse　impact　of　non-IID　problems.　Additionally,　we　design　an　effective　collaborative　learning　mechanism　that　facilitates　the　personalization　preservation　of　local　models　and　significantly　reduces　communication　costs　among　clients.　Empirical　results　of　diverse　GAD　tasks　demonstrate　the　superiority　and　efficiency　of　FGAD.　©　2024　ACM.