Aiming　at　the　problem　of　model　heterogeneity　and　agent　data　scarcity,　a　model　heterogeneous　federated　learning　for　intrusion　detection　(MHFL-ID)　framework　was　proposed.　First,　MHFL-ID　groups　nodes　according　to　model　similarities　and　differences,　that　is,　models　with　the　same　structure　were　grouped　into　the　same　group.　Second,　the　group　leader　centered　isomorphic　aggregation　method　was　used　to　select　the　group　leader　according　to　the　projection　value　of　the　objective　function　and　guide　the　optimization　direction　of　the　nodes　in　the　group　to　enhance　the　modeling　capability　of　the　whole　group　model.　Finally,　the　heterogeneous　aggregation　method　based　on　knowledge　distillation　was　used　between　groups　to　transfer　knowledge　in　heterogeneous　models　with　local　average　soft　label　and　global　soft　label　without　proxy　data.　Comparative　experiments　on　two　datasets,　NSL-KDD　and　UNSW-NB15,　show　that　MHFL-ID　framework　and　the　proposed　method　can　effectively　solve　the　problem　of　heterogeneous　model　aggregation　in　federated　learning　and　achieve　better　results　in　terms　of　accuracy.　©　2024　Beijing　University　of　Technology.　All　rights　reserved.