Federated learning presents a decentralized machine learning paradigm facilitating collaboration among multiple clients by harnessing local computational power and model transmission. Federated Learning (FL) encounters challenges, particularly concerning data leakage due to the lack of robust privacy-preserving mechanisms during storage, transfer, and sharing processes. This poses significant risks to both data owners and suppliers. Existing FL systems often lack robust defense mechanisms to detect and mitigate such poisoning attacks. This paper aims to address this gap by proposing a novel defense strategy that can effectively identify and filter out malicious model updates before they are aggregated into the global model. By providing secure data-sharing platforms, Blockchain enhances the integrity and privacy of federated learning models, thus safeguarding sensitive information within the Internet of Vehicles (IoV) ecosystem. Furthermore, the integration of Homomorphic Encryption (HE) ensures end-to-end encryption of data and model updates, thereby strengthening the security and confidentiality of FL systems. Our approach achieves an Overall Accuracy of 97.20% and a Source-class Accuracy of 95.10% on the MNIST dataset, with a low Attack Success Rate of 0.46%. On the CIFAR-10 dataset, our method achieves an Overall Accuracy of 75.29% and a Source Class Accuracy of 59.30%, with an Attack Success Rate of 10.22%. These results demonstrate the effectiveness of our approach in countering poisoning attacks.