<![CDATA[Federated Learning (FL) has emerged as a promising paradigm for the collaborative training of machine learning models across decentralized devices while preserving data privacy. However, ensuring data security and privacy during model updates remains a critical challenge, particularly in scenarios that involve sensitive data. This study proposes a novel Privacy-Preserving Algorithm (PPA-FL) designed to enhance data security and mitigate privacy leakage risks in FL frameworks. The algorithm integrates advanced encryption techniques, such as homomorphic encryption, with differential privacy to secure model updates without compromising the utility. Furthermore, it incorporates a dynamic noise-adjustment mechanism to adaptively balance privacy and model accuracy. Extensive experiments on benchmark datasets demonstrate that PPA-FL achieves a competitive trade-off between privacy protection and model performance compared to existing methods. The proposed approach is computationally efficient and scalable, making it suitable for real-world applications in healthcare, finance, and the IoT environment. This research contributes to advancing secure data-sharing practices in federated learning, fostering the broader adoption of privacy-preserving machine learning solutions.]]>
