The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) has revolutionized healthcare by enabling predictive diagnostics, personalized treatment, and efficient resource management. However, integrating these technologies into real-world healthcare systems presents significant challenges, particularly concerning data privacy, security, and interoperability across institutions. Federated Learning (FL) has emerged as a promising solution, allowing decentralized model training across multiple healthcare providers without transferring sensitive patient data to a central server. This manuscript explores the integration of FL and privacy-preserving AI techniques within smart healthcare systems, offering a secure and collaborative framework for medical AI applications.
We present a comprehensive review of current FL architectures adapted for healthcare, highlighting their potential in tasks such as disease prediction, medical imaging analysis, and patient monitoring. Furthermore, we examine privacy-preserving mechanisms—including differential privacy, secure multi-party computation, and homomorphic encryption—that fortify FL against data leakage and adversarial attacks. A comparative analysis of these approaches is conducted in terms of scalability, performance, and compliance with healthcare regulations such as HIPAA and GDPR. Additionally, we propose an enhanced FL framework tailored for heterogeneous healthcare environments, capable of addressing data imbalance, device constraints, and communication overhead. Through simulated experiments using benchmark medical datasets, we demonstrate that our framework maintains high model accuracy while significantly reducing privacy risks and computational burden. Our findings underline the transformative potential of federated learning and privacy-preserving AI in enabling secure, equitable, and intelligent healthcare delivery across institutions, paving the way for a new era of collaborative digital medicine.