The use of artificial intelligence (AI) in data-driven medicine has revolutionized healthcare, presenting practitioners with unprecedented tools for diagnosis and personalized therapy. However, this progress comes with a critical concern: the security and privacy of sensitive patient data. As healthcare increasingly leans on AI, the need for robust solutions to safeguard patient information has become more pressing than ever. As AI use begins to increase in healthcare, the specter of data breaches, privacy infringements, and ethical quandaries appear formidable. Patient data, a cornerstone of medical advancement, becomes susceptible to compromise, necessitating a delicate balance between innovation and safeguarding individual privacy. Existing concerns focus on the potential misuse and unauthorized access to this sensitive information, resulting in a significant obstacle to the full realization of AI's potential in healthcare. Federated Learning and Privacy-Preserving in Healthcare AI emerges as the definitive solution to balancing medical progress with patient data security. This carefully curated volume not only outlines the challenges of federated learning but also provides a roadmap for implementing privacy-preserving AI systems in healthcare. By decentralizing the training of AI models, federated learning mitigates the risks associated with centralizing patient data, ensuring that critical information never leaves its original location. Aimed at healthcare professionals, AI experts, policymakers, and academics, this book not only delves into the technical aspects of federated learning but also fosters a collaborative approach to address the multifaceted challenges at the intersection of healthcare and AI. For those seeking a comprehensive guide to navigate the complexities of AI in healthcare while upholding patient privacy, this reference book serves as an indispensable resource.