Federated Learning in Healthcare: An Analytical Study on Privacy-Preserving Data Sharing and Clinical Decision Support

Learning Federated, which permits cooperative model training across dispersed clinical datasets without jeopardizing patient privacy, has become a paradigm shift in healthcare. An analytical examination of FL for data sharing that protects privacy and its possible application in clinical decision support systems is presented in this work. The study investigates how data silos, legal restrictions, and security threats related to traditional centralized methods are addressed via decentralized learning frameworks. The effectiveness of FL algorithms, encryption methods, and secure aggregation strategies in safeguarding private medical data is emphasized. The study also emphasizes how FL-driven models can enhance predictive analytics, therapy customization, and diagnostic accuracy in healthcare applications. Comparative studies and experimental findings show that FL improves the scalability and dependability of clinical decision-making while maintaining anonymity. The results highlight federated learning's potential as a long-term means of promoting data-driven healthcare breakthroughs while maintaining moral and legal observance.