Federated Learning Approaches for Privacy-Preserving AI in Healthcare Data Science

Abstract

The rapid digitization of healthcare has led to an explosion of medical data, offering new opportunities for AI-driven insights. However, privacy concerns and regulatory constraints limit the centralized collection and processing of sensitive patient information. Federated Learning (FL) has emerged as a promising solution, enabling collaborative model training across multiple institutions while preserving data privacy. This paper explores state-of-the-art FL approaches in healthcare, focusing on privacy-preserving techniques, model optimization strategies, and security enhancements. We analyze recent advancements in FL frameworks, their impact on real-world healthcare applications, and existing challenges such as communication overhead, model heterogeneity, and data distribution biases. Furthermore, we discuss the integration of differential privacy, secure multi-party computation, and homomorphic encryption to strengthen privacy guarantees in FL-enabled healthcare AI. The study concludes with future research directions aimed at improving FL scalability, robustness, and regulatory compliance in healthcare environments.