Federated　learning,　leveraging　distributed　data　from　multiple　nodes　to　train　a　common　model,　allows　for　the　use　of　more　data　to　improve　the　model　while　also　protecting　the　privacy　of　original　data.　However,　challenges　still　exist　in　ensuring　privacy　and　security　within　the　interactions.　To　address　these　issues,　this　paper　proposes　a　federated　learning　approach　that　incorporates　blockchain,　homomorphic　encryption,　and　reputation.　Using　homomorphic　encryption,　edge　nodes　possessing　local　data　can　complete　the　training　of　ciphertext　models,　with　their　contributions　to　the　aggregation　being　evaluated　by　a　reputation　mechanism.　Both　models　and　reputations　are　documented　and　verified　on　the　blockchain　through　consensus　process,　which　then　determines　the　rewards　based　on　the　incentive　mechanism.　This　approach　not　only　incentivizes　participation　in　training,　but　also　ensures　the　privacy　of　data　and　models　through　encryption.　Additionally,　it　addresses　security　risks　associated　with　both　data　and　network　attacks,　ultimately　leading　to　a　highly　accurate　trained　model.　To　enhance　the　efficiency　of　learning　and　the　performance　of　the　model,　a　joint　adaptive　aggregation　and　resource　optimization　algorithm　is　introduced.　Finally,　simulations　and　analyses　demonstrate　that　the　proposed　scheme　enhances　learning　accuracy　while　maintaining　privacy　and　security.　IEEE