Multi-label　propagation　algorithms　(MLPAs)　aim　to　find　vertex　communities　in　a　complex　network　or　a　cloud　system　by　propagating　and　updating　vertex　labels,　which　have　been　widely　applied　in　customer　recommendation,　protein　molecule　discovery,　and　criminal　tracking.　As　more　and　more　people　are　concerned　about　the　leakage　of　their　sensitive　information,　detecting　communities　without　disclosing　personal　privacy　has　become　a　hot　topic　in　complex　network　analysis.　The　existing　anonymization-based　community　detection　methods　have　to　modify　the　network　structure　to　protect　the　sensitive　vertices　or　links,　which　complicates　the　recognition　of　true　communities　and　incurs　substantial　accuracy　loss.　In　this　article,　we　first　propose　a　federated　graph　learning　model　(FGLM)　for　distributed　privacy-preserving　network　data　mining.　Second,　a　federated　MLPA　for　distributed　and　attributed　networks　is　implemented　by　adapting　a　standalone　MLPA　to　FGLM　to　verify　the　model＇s　effectiveness.　We　develop　a　label　perturbation　strategy　to　conceal　vertex　degrees　in　distributed　label　updating　and　employ　a　homomorphic　encryption　system　to　protect　label　weights　exchanged　between　the　participants.　The　experiments　on　real-world　and　synthetic　datasets　demonstrate　that　the　new　algorithm　achieves　zero　accuracy　loss　and　more　than　200%　higher　accuracy　than　the　simple　distributed　MLPA　without　federated　learning.