FEDERATED LEARNING FOR SECURE SMART HEALTHCARE: A PRIVACY-PRESERVING ARTIFICIAL INTELLIGENCE FRAMEWORK

Artificial Intelligence (AI) has transformed healthcare by enabling intelligent diagnosis, predictive analytics, disease monitoring, and personalized treatment. However, the increasing use of patient data has raised serious concerns regarding privacy, security, and regulatory compliance. Federated Learning (FL) has emerged as a promising machine learning paradigm that allows multiple healthcare institutions to collaboratively train AI models without sharing sensitive patient data. Instead of transmitting raw datasets, only model parameters are exchanged, thereby preserving confidentiality while maintaining learning performance. This paper presents a comprehensive study of Federated Learning in smart healthcare systems, discussing its architecture, applications, security mechanisms, advantages, challenges, and future research opportunities. The proposed privacy-preserving framework integrates secure aggregation, differential privacy, and blockchain-based verification to improve trust and model integrity. Experimental discussions indicate that federated learning significantly reduces privacy risks while achieving competitive predictive performance compared to centralized learning. The paper concludes that FL will become one of the foundational technologies for next-generation intelligent healthcare systems.