Federated　learning　is　an　innovative　distributed　machine　learning　technology.　Each　participant　saves　private　data　locally　and　only　sends　parameter　information　during　model　training　to　the　aggregation　server　without　directly　sharing　the　original　data　so　as　to　ensure　data　security　and　privacy.　However,　in　the　process　of　model　training,　a　single　aggregation　server　is　vulnerable　to　poisoning　attacks　or　single　points　of　failure,　and　malicious　server　adversaries　can　also　have　a　substantial　negative　impact　on　the　model＇s　prediction.　Therefore,　in　this　paper,　we　propose　a　decentralized　asynchronous　federated　learning　framework　based　on　blockchain　technology,　which　uses　blockchain　to　replace　the　parameter　server　in　traditional　federated　learning,　and　uses　a　homomorphic　encryption　algorithm　to　encrypt　the　model　gradient　and　other　parameters　in　the　training　process.　In　addition,　this　paper　also　proposes　a　model　performance　verification　mechanism　to　ensure　the　accuracy　of　model　training.　After　experimental　verification,　the　framework　achieves　better　training　results　on　the　MNIST　dataset　and　CIFAR-10　dataset　and　effectively　avoids　malicious　attacks　from　the　central　server.　©　2024　IEEE.