The book intends to present emerging Federated Learning (FL) based architectures, frameworks, and models in Internet-of-Medical Things (IoMT) applications. It intends to build up onto the basics of healthcare industry, the current data sharing requirements, and security and privacy issues in medical data sharing. Once IoMT is presented, the shift is towards the proposal of privacy-preservation in IoMT, and explains how FL presents a viable solution to these challenges. The claims are supported through lucid illustrations, tables, and examples that presents effective and secured FL schemes, simulations, and practical discussion on use-case scenarios in simple manner. The book tends to create opportunities of healthcare communities to build effective FL solutions around the presented themes, and the divergent ideas that prosper from reading the book. It also intends to present breakthroughs and foster innovation in FL-based research, specifically in IoMT domain. The emphasis is on understanding the contributions of IoMT in healthcare analytics and its aim is to give the insights including evolution, research directions, challenges and the way to empower healthcare services through federated learning. The book also intends to cover the issues of ethical and social issues around the recent advancements in the field of decentralized Artificial Intelligence. The book is mainly intended for undergraduates, post-graduates, researchers, and healthcare professionals who wish to learn FL-based solutions right from scratch, and build practical FL solutions in different IoMT verticals.