In　the　modern　interconnected　world,　intelligent　networks　and　computing　technologies　are　increasingly　being　incorporated　in　industrial　systems.　However,　this　adoption　of　advanced　technology　has　resulted　in　increased　cyber　threats　to　cyber-physical　systems.　Existing　intrusion　detection　systems　are　continually　challenged　by　constantly　evolving　cyber　threats.　Machine　learning　algorithms　have　been　applied　for　intrusion　detection.　In　these　techniques,　a　classification　model　is　trained　by　learning　cyber　behavior　patterns.　However,　these　models　typically　require　considerable　high-quality　datasets.　Limited　attack　samples　are　available　because　of　the　unpredictability　and　constant　evolution　of　cyber　threats.　To　address　these　problems,　we　propose　a　novel　federated　Execution　&　Evaluation　dual　network　framework　(EEFED),　which　allows　multiple　federal　participants　to　personalize　their　local　detection　models　undermining　the　original　purpose　of　Federated　Learning.　Thus,　a　general　global　detection　model　was　developed　for　collaboratively　improving　the　performance　of　a　single　local　model　against　cyberattacks.　The　proposed　personalized　update　algorithm　and　the　optimizing　backtracking　parameters　replacement　policy　effectively　reduced　the　negative　influence　of　federated　learning　in　imbalanced　and　non-i.i.d　distribution　of　data.　The　proposed　method　improved　model　stability.　Furthermore,　extensive　experiments　conducted　on　a　network　dataset　in　various　cyber　scenarios　revealed　that　the　proposed　method　outperformed　single　model　and　state-of-the-art　methods.