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Federated Learning in Health Care Technology

By Mridha, Muhammad Firoz

Federated Learning in Health Care Technology Medicine

Federated Learning in Health Care Technology

0 - Default Title
Description
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.
Product details
Number of Pages:
244
Release Date:
2025-11-02
Publication Date:
2025-11-02
Publisher:
Springer
Languages:
Original: English
ISBN10:
9819683521
ISBN13:
9789819683529
GPSR Manufacturer Reference:
[email protected]
Weight:
533 g
Height:
160 cm
Width:
241 cm
Thickness:
19 cm
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