Federated Learning is currently an emerging technology in the field of Machine Learning. Federated Learning is a structure which trains a centralized model for a given assignment, where the data is de-centralized across different edge devices or servers. This enables preservation of the confidentiality of data on various edge devices, as only the updated outcomes of the models are shared with the centralized model. This means the data can remain on each edge device, while we can still train a model using that data. Federated Learning has greatly increased the potential to transmute data in the healthcare industry, enabling healthcare professionals to improve treatment of patients. This book comprises chapters on applying Federated models in the field of healthcare industry. Federated Learning mainly concentrates on securing the privacy of data by training local data in a shared global model without putting the training data in a centralized location. The importance of Federated Learning lies in its innumerable uses in health care that ranges from maintaining the privacy of raw data of the patients, discover clinically alike patients, forecasting hospitalization due to cardiac events impermanence and probable solutions to the same. The goal of this edited book is to provide a reference guide to the theme. Chapter 1 explores the “Fundamentals of Federated Learning,” laying the groundwork for readers to comprehend the underlying concepts and principles that govern this groundbreaking methodology. Moving forward, Chapter 2, “Federated Learning and its Classifications,” offers a comprehensive understanding of the varied approaches and techniques employed in different scenarios... Chapter 9 focuses on Federated Learning using TensorFlow, one of the most popular open-source Machine Learning frameworks.