With　the　rapid　development　of　technology,　the　number　of　network　edge　devices　with　the　capability　of　computation　and　memory　is　increasing,　and　the　volume　of　the　generated　data　is　growing　exponentially,　which　makes　it　difficult　for　a　centralized　processing　model　with　cloud　computing　as　the　core　to　efficiently　process　data　generated　by　edge　devices.　Not　only　will　the　network　delay　increase,　but　the　data　is　likely　to　be　leaked　on　the　upload　link,　and　data　security　cannot　be　guaranteed.　In　addition,　due　to　the　diversity　of　edge　devices　and　the　continuous　enrichment　of　data　representation　methods,　multi-modal　data　exists　widely.　The　processing　of　multi-source　heterogeneous　data　collected　by　different　edge　devices　has　become　an　urgent　problem　in　big　data　research.　In　order　to　make　full　use　of　heterogeneous　data　on　edge　devices　and　solve　the　problem　of　＇data　communication　barriers＇　caused　by　data　privacy　in　edge　computing,　in　this　paper　we　propose　a　novel　fusion　algorithm　for　multi-source　heterogeneous　data　based　on　Tucker　decomposition　in　federated　learning.　For　the　fusion　problem　of　heterogeneous　data　without　interaction　in　federated　learning,　the　proposed　algorithm　introduces　Tucker　decomposition　theory　to　capture　the　multi-dimensional　characteristics　of　heterogeneous　data　by　constructing　a　high-order　tensor.　Finally,　the　effectiveness　of　this　algorithm　is　verified　on　the　MOSI　dataset.　©　2022,　Science　Press.　All　right　reserved.