Federated Learning (FL) is an emerging distributed machine learning paradigm that enables collaborative model training across multiple healthcare institutions without requiring centralized access to sensitive patient data. In modern healthcare systems, data is generated from electronic health records (EHRs), wearable devices, diagnostic imaging systems, and clinical monitoring tools. Centralized analytics approaches raise significant concerns regarding privacy, security, and regulatory compliance under frameworks such as HIPAA and GDPR. Federated learning addresses these challenges by allowing local model training at each institution while sharing only encrypted gradients or model parameters with a central aggregation server. This ensures that raw patient data never leaves its source environment, significantly reducing the risk of data breaches and unauthorized access
Recent advancements in federated learning frameworks such as TensorFlow Federated, Flower, PySyft, and NVIDIA Clara have improved scalability, security, and interoperability in healthcare applications. These frameworks support privacy-enhancing technologies including differential privacy, secure aggregation, and homomorphic encryption. Applications of FL in healthcare include disease prediction, medical image classification, drug discovery, and remote patient monitoring. Despite its advantages, challenges such as communication overhead, non-IID data distribution, adversarial attacks, and system heterogeneity remain critical issues. This study explores federated learning frameworks for privacy-preserving healthcare analytics reviews existing literature, and proposes a structured methodology for secure distributed model training. The research highlights future directions integrating blockchain and edge computing with federated learning for enhanced healthcare intelligence systemsFederated Learning
