In a federated learning environment, data interaction and sharing between nodes are inevitable. Malicious attackers may tamper with the data of certain nodes or even train models, posing a security threat to entities. Among these security attacks, backdoor attacks are the most covert. On the basis of existing research, this article designs a federated learning convergence wheel backdoor defense method and applies it to the field of vehicle networking. This method can effectively defend against backdoor attacks.