Revolutionizing Health Management Through Federated Learning and AI

The integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies is transforming healthcare by enabling continuous, personalized health monitoring and proactive medical interventions. However, the sensitive nature of health data presents critical privacy and security challenges. This chapter explores the role of Federated Learning (FL) as a privacy-preserving machine learning paradigm that enables collaborative model training across distributed healthcare data sources without compromising patient confidentiality. We examine the synergy between AI, IoT, and FL in applications such as chronic disease management, remote monitoring, and personalized treatment. Key privacy-enhancing techniques like differential privacy, homomorphic encryption, and secure aggregation are discussed alongside real-world use cases and FL architectures. The chapter also addresses infrastructure enablers including edge computing and 5G/6G connectivity, as well as the challenges of scalability, trust, and adversarial threats.