Owing　to　the　strong　protection　of　data　privacy,　federated　learning　(FL)　has　become　a　key　method　for　achieving　intelligent　decision　making　in　smart　homes.　However,　under　realistic　conditions,　such　as　differentiated　requirements　and　heterogeneous　service　environments,　FL　in　smart　homes　faces　the　problem　of　non-independent　and　identically　distributed　(non-IID)　data　and　uneven　computing　power,　which　leads　to　the　poor　adaptability　of　global　models.　To　address　this　issue,　this　study　proposes　a　cluster　FL　architecture　based　on　edge-cloud　collaboration.　First,　a　Gaussian　mixture　model-based　cluster　FL　is　proposed　to　improve　the　model　accuracy　by　clustering　features　on　the　FL　dataset　and　ensuring　an　independent　identical　distribution　of　the　data.　Subsequently,　a　model　training　strategy　based　on　edge-cloud　collaboration　is　proposed　to　achieve　the　sharing　of　edge-cloud　computing　power　by　split　learning,　which　provides　sufficient　computing　power　for　model　training.　The　simulation　results　show　that　the　proposed　architecture　improves　the　accuracy　of　global　models　while　ensuring　normal　network　service　provision.　Author