Federated Learning in Health Care Technology

This book offers an in-depth exploration of federated learning (FL), a groundbreaking approach that facilitates collaborative data analysis while ensuring patient privacy and data security. As healthcare systems worldwide generate vast amounts of data, the challenge lies in harnessing this information without compromising confidentiality. Federated learning addresses this by allowing multiple institutions to collaborate on machine learning models without sharing sensitive data. In this context, the authors delve into the foundational principles of FL, illustrating how it enables the aggregation of decentralized data to improve diagnostic accuracy, develop personalized treatment plans, and enhance overall healthcare outcomes. The authors present real-world applications across various medical fields, from predictive analytics in chronic disease management to precision medicine and beyond. Additionally, the authors discuss the ethical and regulatory landscapes, providing insights into the challenges and solutions associated with implementing FL in healthcare. This book is designed for a diverse audience, including researchers, healthcare practitioners, data scientists, and policymakers. It aims to bridge the gap between cutting-edge technology and practical medical applications, offering a comprehensive guide to leveraging FL for healthcare innovation.

List of contents

Federated Learning in Health Care Technology Challenges, Solutions and Opportunities. Decentralized Tumor Classification with Federated Learning A Privacy-Preserving Approach.- Transforming Healthcare Analytics.- Privacy-Preserving Kidney Stone Detection from X-ray Image using Federated Learning. Federated Learning for Privacy-Preserving Healthcare Analytics: A Novel Framework for Fraud Detection in Healthcare.

Product details

• Assisted by: Dey (Editor), Nilanjan Dey (Editor), Muhammad Firoz Mridha (Editor), Muhammad Firoz Mridha (Editor)
• Publisher: Springer, Berlin
• Languages: English
• Product format: Hardback
• Release: 30.08.2026
• EAN: 9789819683529
• ISBN: 978-981-9683-52-9
• No. of pages: 278
• Illustrations: XII, 278 p. 40 illus. in color.
• Series: Studies in Computational Intelligence
• Subjects: Natural sciences, medicine, IT, technology > Technology > General, dictionaries; machine learning, Maschinelles Lernen, Deep Learning, Maschinelles Sehen, Bildverstehen, Computer Vision, Computational Intelligence, Federated Learning, Privacy Preserving, Disease Classification, Disease Identification