Intrusion　detection　methods　are　crucial　means　to　mitigate　network　security　issues.　However,　the　challenges　posed　by　large-scale　complex　network　environments　include　local　information　islands,　regional　privacy　leaks,　communication　burdens,　difficulties　in　handling　heterogeneous　data,　and　storage　resource　bottlenecks.　Federated　learning　has　the　potential　to　address　these　challenges　by　leveraging　widely　distributed　and　heterogeneous　data,　achieving　load　balancing　of　storage　and　computing　resources　across　multiple　nodes,　and　reducing　the　risks　of　privacy　leaks　and　bandwidth　resource　demands.　This　paper　reviews　the　process　of　constructing　federated　learning　based　intrusion　detection　system　from　the　perspective　of　intrusion　detection.　Specifically,　it　outlines　six　main　aspects:　application　scenario　analysis,　federated　learning　methods,　privacy　and　security　protection,　selection　of　classification　models,　data　sources　and　client　data　distribution,　and　evaluation　metrics,　establishing　them　as　key　research　content.　Subsequently,　six　research　topics　are　extracted　based　on　these　aspects.　These　topics　include　expanding　application　scenarios,　enhancing　aggregation　algorithm,　enhancing　security,　enhancing　classification　models,　personalizing　model　and　utilizing　unlabeled　data.　Furthermore,　the　paper　delves　into　research　content　related　to　each　of　these　topics　through　in-depth　investigation　and　analysis.　Finally,　the　paper　discusses　the　current　challenges　faced　by　research,　and　suggests　promising　directions　for　future　exploration.