Secure IoT Healthcare: Federated Learning and Blockchain for Privacy-Preserving AI

This chapter delves into the integration of Federated Learning (FL) and Blockchain to create a secure and privacy-preserving framework for AI in healthcare. It begins with an overview of the challenges in healthcare data management, such as data fragmentation, security risks, and regulatory constraints. The text then explores the advantages of combining Blockchain and Federated Learning, highlighting their potential to enhance trust, transparency, and security in healthcare AI systems. The proposed architecture is detailed, including dataset selection, hybrid machine learning methods, and system integration. Performance and simulation results are evaluated, demonstrating the effectiveness of the proposed approach. The chapter also discusses the challenges in implementing Federated Learning in healthcare, such as privacy concerns, computational constraints, and interoperability issues. It concludes with key findings and future research directions for enhancing privacy-preserving AI in healthcare. The chapter provides a comprehensive review of related works, showcasing various applications of FL and Blockchain in healthcare, and comparing the proposed approach with existing benchmarks. The evaluation of performance and simulation results highlights the superior accuracy and efficiency of the proposed model, making it a promising solution for secure and privacy-preserving AI in healthcare.