Published Paper Details:

: The increasing integration of artificial intelligence (AI) in healthcare has significantly advanced disease prediction, diagnosis, and personalized treatment planning. However, the development of high-performance machine learning models is critically dependent on access to large-scale, high-quality medical datasets, which are often restricted due to stringent privacy regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR). These constraints lead to fragmented data silos across institutions, limiting the generalization capability of conventional centralized learning approaches. Federated Learning (FL) has emerged as a transformative paradigm that enables collaborative model training across multiple decentralized entities without requiring the exchange of raw data, thereby preserving data privacy and ownership [1]. This paper presents a comprehensive federated learning framework for privacy-preserving medical predictions, designed to address key challenges in secure collaborative healthcare analytics. The proposed system integrates advanced privacy-enhancing technologies, including secure aggregation protocols [2], differential privacy mechanisms [3], and homomorphic encryption techniques [4], to ensure that sensitive patient information remains protected throughout the training process. Additionally, the framework incorporates communication-efficient optimization strategies and adaptive federated averaging algorithms to mitigate issues related to data heterogeneity and network constraints [5]. Extensive experimental evaluations conducted on distributed healthcare datasets demonstrate that federated models achieve comparable predictive performance to traditional centralized approaches, with accuracy levels exceeding 95% in disease classification tasks, while ensuring zero raw data exposure. Furthermore, the framework maintains strict compliance with regulatory standards and significantly reduces the risk of data breaches and re-identification attacks [6]. The results highlight the feasibility, scalability, and robustness of federated learning in real-world healthcare environments, establishing it as a viable solution for next-generation privacy-preserving medical AI systems.