The　recent　expansion　of　the　IoT　ecosystem　has　not　only　significantly　increased　connectivity　but　also　introduced　new　security　challenges.　To　address　emerging　security　challenges,　this　study　proposes　a　framework　that　merges　the　decentralized　methodologies　of　federated　learning　(FL)　and　Blockchain.　The　framework　is　rigorously　tested　and　validated　on　the　N-BaIoT　Dataset　employing　dense　neural　networks　(DNNs)　and　logistic　regression　(LR).　This　approach　decentralizes　the　training　of　machine　learning　(ML)　models　by　distributing　the　process　across　individual　IoT　devices,　this　enhances　the　security　and　privacy　of　data.　The　use　of　Blockchain　ensures　transparent　and　secure　management　of　these　decentralized　models,　adding　an　extra　layer　of　protection　against　tampering.　In　addition,　this　research　introduces　two　novel　metrics,　namely　the　Security　Efficacy　Metric　and　the　Comparative　Improvement　Factor,　which　provide　a　quantitative　foundation　for　evaluating　the　performance　of　the　proposed　framework.　The　examination　of　the　proposed　framework　through　LR　and　DNNs　demonstrates　significant　results.　The　LR　model　achieved　a　global　accuracy　of　99.98%,　with　an　average　client　data　size　of　440.95　MB　and　a　model　size　of　0.00088　MB.　Meanwhile,　the　DNN　model　exhibited　a　global　accuracy　of　99.99%,　with　an　average　client　data　size　of　551.95　MB　and　a　model　size　of　0.09　MB.　This　research　contributes　to　IoT　security　by　integrating　LR　and　DNNs　within　the　FL　setup,　complemented　by　blockchain　technology,　signifying　a　substantial　advancement　in　the　dynamic　IoT　ecosystem.