A verifiable decentralized federated learning method was proposed in a decentralized network containing an unknown number of Byzantine users, aiming at the problem that in federated learning, Byzantine users send arbitrary error messages that contaminate the global model and affect the security and effectiveness of federated learning. The SCORE function was employed in the proposed method, to assess the impact of unknown attribute users on the global model performance based on a validation dataset. Thereby malicious model updates were excluded and security gradient aggregation for safe and efficient federated learning was implemented. A thresholding mechanism was applied to the score results from the SCORE function to lower the error rate in user attribute classification and increase the fault tolerance for honest users. Theoretical demonstrations confirmed the convergence of the verifiable decentralized federated learning algorithm, and a considerable number of numerical experiments substantiated the method’s robustness concerning both the quantity of Byzantine users and the types of attacks. Results showed that the method achieved optimal classification accuracy compared to other fault-tolerant algorithms in the presence of same Byzantine attack conditions.