In　order　to　address　the　issue　of　inadequate　training　efficiency　and　subpar　model　performance　encountered　by　Internet　of　things　(IoT)　devices　when　dealing　with　resource　constraints　and　non-independent　and　identically　distributed　(Non-IID)　data,　a　novel　personalized　pruning　federated　learning　frame　work　for　IoT　intrusion　detection　was　put　forth.　Initially,　a　channel　importance　scoring-based　structured　pruning　strategy　was　proposed,　facilitating　the　generation　of　sub-models　to　be　disseminated　to　resource-limited　clients,　thereby　harmonizing　model　accuracy　and　complexity.　Subsequently,　an　innovative　heterogeneous　model　aggregation　algorithm　was　introduced,　utilizing　similarity-weighted　coefficients　for　channel　averaging,　thereby　effectively　mitigating　the　adverse　effects　of　Non-IID　data　during　the　model　aggregation　process.　Ultimately,　experimental　results　derived　from　the　network　intrusion　dataset　BoT-IoT　substantiate　that,　relative　to　existing　methods,　the　proposed　method　notably　curtails　the　time　expenditure　of　resource-constrained　clients,　and　improves　processing　speed　by　20.82%,　while　enhancing　the　accuracy　of　intrusion　detection　by　0.86%　in　Non-IID　conditions.　©　2024　Editorial　Board　of　Journal　on　Communications.　All　rights　reserved.