With the rapid development and widespread adoption of the Internet of Things, cloud computing, and artificial intelligence, data security has become a critical concern. Homomorphic encryption, as an effective solution to privacy protection, enables computations to be performed directly on encrypted data without decryption. It has thus become a powerful cryptographic tool and is widely applied to secure aggregation in federated learning. However, as federated learning continues to evolve, the security of model transmission has emerged as a pressing challenge, and verifying the integrity and authenticity of the transmitted models remains an open problem. In this paper, we propose a verifiable and privacy-preserving federated learning (VPFL) framework, which simultaneously ensures privacy preservation and model verifiability while also supporting regulatory auditing. Based on a VPFL model trained on the MNIST dataset, our approach achieves a prediction accuracy of 90.67%, while the experimental results demonstrate that the computational overhead remains within an acceptable range.