Federated Learning in Internet of Medical Things: A Comprehensive Guide

By Pronaya Bhattacharya, this book aims to provide a detailed overview of emerging Federated Learning (FL) based architectures, frameworks, and models in Internet of Medical Things (IoMT) applications. It delves into the basics of the healthcare industry, the current data sharing requirements, and the security and privacy issues in medical data sharing.

Understanding the Healthcare Industry and IoMT

The healthcare industry is undergoing a significant transformation with the advent of technology. The Internet of Medical Things (IoMT) is a network of medical devices, applications, and services that are connected through the internet. This interconnected system enables the efficient exchange of data, improving patient care and healthcare services.

Privacy-Preservation in IoMT and the Role of Federated Learning

One of the major challenges in IoMT is ensuring the privacy and security of patient data. Federated Learning (FL) presents a viable solution to these challenges. FL is a decentralized approach to machine learning that enables multiple entities to collaboratively train a model without sharing their local data. This approach ensures that patient data remains private and secure while still allowing for the benefits of collaborative learning.

FL-Based Solutions in IoMT: Illustrations, Tables, and Examples

This book provides lucid illustrations, tables, and examples that present effective and secured FL schemes, simulations, and practical discussion on use-case scenarios in a simple manner. These resources are designed to help healthcare communities build effective FL solutions around the presented themes and foster innovation in FL-based research, specifically in the IoMT domain.

Empowering Healthcare Services through Federated Learning

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. By leveraging FL, healthcare services can be improved through better data sharing, enhanced patient care, and more accurate predictive models.

Ethical and Social Issues in Decentralized Artificial Intelligence

This book also covers the ethical and social issues around the recent advancements in the field of decentralized Artificial Intelligence. It is crucial to address these issues to ensure that AI systems are developed and used in ways that align with human values and promote social welfare.

Target Audience

This 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. It provides a comprehensive guide for those interested in understanding the basics of FL and its applications in the healthcare industry.