Advancing AI Integration in Healthcare Using Federated Learning

Abstract

With the growing demand for secure, intelligent, and collaborative healthcare systems, Federated Learning (FL) has emerged as a transformative approach for developing AI models without exposing sensitive patient data. This chapter provides a detailed overview of FL's core architecture and its specialized variants—such as hierarchical, asynchronous, and personalized FL—tailored to healthcare's distributed and privacy-sensitive landscape. It explores privacy-enhancing mechanisms including local differential privacy, secure aggregation, and homomorphic encryption, all critical for training in heterogeneous environments. The integration of FL with blockchain, edge computing, and explainable AI (XAI) demonstrates how these technologies strengthen traceability, transparency, and real-time intelligence. Key challenges such as regulatory inconsistencies, infrastructural constraints, and model drift are critically examined. The chapter envisions a federated AI ecosystem promoting equitable, privacy-aware innovation through institutional collaboration and scalable deployment strategies.

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