Federated Learning for Internet of Medical Things

This book intends to present emerging Federated Learning (FL)-based architectures, frameworks, and models in Internet of Medical Things (IoMT) applications. It intends to build on the basics of the healthcare industry, the current data sharing requirements, and security and privacy issues in medical data sharing. Once IoMT is presented, the book shifts 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 present effective and secured FL schemes, simulations, and practical discussion on use-case scenarios in a simple manner. The book intends to create opportunities for healthcare communities to build effective FL solutions around the presented themes, and to support work in related areas that will benefit from reading the book. It also intends to present breakthroughs and foster innovation in FL-based research, specifically in the IoMT domain. The emphasis of this book is on understanding the contributions of IoMT to healthcare analytics, and its aim is to provide insights including evolution, research directions, challenges, and the way to empower healthcare services through federated learning. The book also intends to cover the 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.

Pronaya Bhattacharya, Ashwin Verma, Sudeep Tanwar

1. Potentials of Internet of Medical Things: Fundamentals and Challenges, 2. Artificial Intelligence Applications for IoMT, 3. Privacy and Security in Internet of Medical Things, 4. IoMT Implementation: Technological Overview for Healthcare Systems, 5. A New Method of 5G-Based Mobile Computing for IoMT Applications, 6. Trusted Federated Learning Solutions for Internet of Medical Things, 7. Early Prediction of Prevalent Diseases Using IoMT, 8. Trusted Federated Learning for Internet of Medical Things: Solutions and Challenges, 9. Security and Privacy Solutions for Healthcare Informatics, 10. IoT-Based Life-Saving Devices Equipped with Ambu Bags for SARS-CoV-2 Patients, 11. Security and Privacy in Federated Learning-Based Internet of Medical Things, 12. Use-Cases and Scenarios for Federated Learning Adoption in IoMT, 13. Blockchain for Internet of Medical Things

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