Community　detection　is　a　popular　research　topic　in　complex　network　analysis,　which　can　be　applied　in　many　real-world　scenarios　such　as　disease　prediction.　With　the　increase　of　people＇s　awareness　of　privacy　protection,　more　and　more　laws　enforce　the　protection　of　sensitive　information　while　transferring　data.　The　anonymization-based　community　detection　methods　have　to　sacrifice　accuracy　for　privacy　protection.　In　this　paper,　we　first　propose　a　standalone　clique　percolation　algorithm　to　detect　overlapping　communities　on　attributed　networks.　A　clique　similarity　metric　is　designed　to　percolate　cliques　accurately.　Second,　we　develop　a　federated　clique　percolation　algorithm　to　detect　overlapping　communities　on　distributed　attributed　networks.　Perturbation　strategy　and　homomorphic　encryption　are　used　to　protect　network　privacy.　The　experiments　on　real-world　and　artificial　datasets　demonstrate　that　the　federated　clique　percolation　algorithm　achieves　identical　results　to　the　standalone　ones　and　realizes　higher　accuracy　than　the　simple　distributed　ones　without　federating　learning.