In　the　face　of　large-scale,　diverse,　and　time-evolving　data,　as　well　as　machine　learning　tasks　in　industrial　production　processes,　a　Federated　Incremental　Learning(FIL)　and　optimization　method　based　on　information　entropy　is　proposed　in　this　paper.　Within　the　federated　framework,　local　computing　nodes　utilize　local　data　for　model　training,　and　compute　the　average　entropy　to　be　transmitted　to　the　server　to　assist　in　identifying　class-incremental　tasks.　The　global　server　then　selects　local　nodes　for　current　round　training　based　on　the　locally　provided　average　entropy　and　makes　decisions　on　task　incrementality,　followed　by　global　model　deployment　and　aggregation　updates.　The　proposed　method　combines　average　entropy　and　thresholds　for　nodes　selection　in　various　situations,　achieving　stable　model　learning　under　low　average　entropy　and　incremental　model　expansion　under　high　average　entropy.　Additionally,　convex　optimization　is　employed　to　adaptively　adjust　aggregation　frequency　and　resource　allocation　in　resource-constrained　scenarios,　ultimately　achieving　effective　model　convergence.　Simulation　results　demonstrate　that　the　proposed　method　accelerates　model　convergence　and　enhances　training　accuracy　in　different　scenarios.　©　2024　Science　Press.　All　rights　reserved.