The　Multi-Label　Propagation　Algorithm　(MLPA)　identifies　and　reveals　community　structure　by　passing　labels　between　network　nodes,　and　is　also　suitable　for　dealing　with　complex　networks　with　overlapping　communities.　Due　to　its　flexibility　and　effectiveness,　the　algorithm　has　been　successfully　applied　in　a　number　of　fields,　including　image　segmentation,　text　classification,　and　bioinformatics.　In　today’s　society　where　personal　privacy　protection　is　increasingly　important,　how　to　detect　communities　without　revealing　sensitive　information　has　become　a　hot　issue　in　the　field　of　network　analysis.　Existing　privacy-preserving　multi-label　propagation　algorithms　primarily　rely　on　anonymization　and　homomorphic　encryption　techniques.　While　homomorphic　encryption　can　protect　privacy,　the　complex　encryption　and　decryption　processes　incur　significant　computational　costs,　making　it　challenging　to　achieve　efficient　computation　while　ensuring　accuracy　and　privacy.　In　this　paper,　we　propose　a　Secure　and　Efficient　Federated　Multi-Label　Propagation　Algorithm　(SEFMLPA)　that　combines　an　anonymization　strategy　with　a　secret　sharing　strategy,　considering　the　attribute　similarities　between　nodes　to　ensure　privacy,　accuracy,　and　efficiency.　The　experimental　results　indicate　that　SEFMLPA　achieves　an　accuracy　comparable　to　the　latest　algorithms　and　reduces　runtime　by　80%.　These　significant　improvements　validate　the　effectiveness　and　superiority　of　our　approach.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.