This book provides a practical guide to Federated Deep Learning for healthcare including fundamental concepts, framework, and the applications comprising of domain adaptation, model distillation, and transfer learning. It covers concerns in model fairness, data bias, regulatory compliance, and ethical dilemmas. It investigates several privacy-preserving methods like homomorphic encryption, secure multi-party computation, and differential privacy. It will enable readers to build and implement Federated Learning systems that safeguard private medical information. This book is aimed at graduate students and researchers in Federated Learning, Data Science, AIMachine Learning, and healthcare.