Intrusion　detection　based　on　federated　learning　allows　the　sharing　of　more　high-quality　attack　samples　to　improve　the　intrusion　detection　performance　of　local　models　while　preserving　the　privacy　of　local　data.　Most　research　on　federated　learning　intrusion　detection　requires　local　models　to　be　homogeneous.　However,　in　practical　scenarios,　local　models　often　include　both　homogeneous　and　heterogeneous　models　due　to　differences　in　hardware　capabilities　and　business　requirements　among　nodes.　Additionally,　there　is　still　room　for　improvement　in　the　accuracy　of　recognizing　novel　attacks　in　existing　researches.　To　address　the　challenges　mentioned　above,　we　propose　a　Group-based　Federated　Knowledge　Distillation　Intrusion　Detection　approach.　First,　through　a　step-by-step　grouping　method,　we　achieve　the　grouping　effect　of　intra-group　homogeneity　and　inter-group　heterogeneity,　laying　the　foundation　for　reducing　the　aggregation　difficulty　in　intra-group　homogenous　aggregation　and　inter-group　heterogeneous　aggregation.　Second,　in　intra-group　homogenous　aggregation,　a　dual-objective　optimization　model　is　employed　to　quantify　the　learning　quality　of　local　models.　Weight　coefficients　are　assigned　based　on　the　learning　quality　to　perform　weighted　aggregation.　Lastly,　in　inter-group　heterogeneous　aggregation,　the　group　leader　model＇s　learning　quality　is　used　to　classify　and　aggregate　local　soft　labels,　generating　global　soft　labels.　Group　leader　models　utilize　global　soft　labels　for　knowledge　distillation　to　acquire　knowledge　from　heterogeneous　models.　Experimental　results　on　NSL-KDD　and　UNSW-NB　datasets　demonstrate　the　superiority　of　our　proposed　method　over　other　algorithms.