Community　structure　is　a　typical　characteristic　of　complex　networks.　Finding　communities　in　complex　networks　has　many　important　applications,　such　as　the　advertisement　and　recommendation　based　on　social　networks　and　the　discovery　of　new　protein　molecules　in　biological　networks,　which　make　it　a　hot　topic　in　the　field　of　complex　network　analysis.　With　the　increasing　concerns　about　the　leakage　of　personal　privacy,　discovering　communities　spread　across　the　local　networks　owned　by　multiple　participants　accurately　while　preserving　each　participant＇s　privacy　has　become　an　emerging　challenge　in　distributed　community　detection.　In　this　article,　we　propose　a　general　federated　graph　learning　model　for　privacy-preserving　distributed　graph　learning　and　develop　two　federated　clique　percolation　algorithms　(CPAs)　based　on　it　to　discover　overlapping　communities　distributed　across　multiple　participants＇　local　networks　without　disclosing　any　participant＇s　network　privacy.　Homomorphic　encryption　and　hash　operation　are　used　in　combination　to　protect　the　privacy　of　the　vertices　and　edges　of　each　local　network.　Furthermore,　vertex　attributes　are　involved　in　the　calculation　of　clique　similarity　and　clique　percolation　when　dealing　with　attributed　networks.　The　experimental　results　on　real-world　and　artificial　datasets　demonstrate　that　the　proposed　algorithms　achieve　identical　results　to　those　of　their　stand-alone　counterparts　and　more　than　200%　higher　accuracy　than　the　simple　distributed　CPAs　without　federating　learning.