Federated Deep Learning for Healthcare: A Practical Guide with Challenges and Opportunities (Advances in Smart Healthcare Technologies)

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Synopsis
This book provides a practical guide to federated deep learning for healthcare including fundamental concepts, framework, and the applications comprising domain adaptation, model distillation, and transfer learning. It covers concerns in model fairness, data bias, regulatory compliance, and ethical dilemmas. It investigates several privacy-preserving methods such as homomorphic encryption, secure multi-party computation, and differential privacy. It will enable readers to build and implement federated learning systems that safeguard private medical information.Features: Offers a thorough introduction of federated deep learning methods designed exclusively for medical applications. Investigates privacy-preserving methods with emphasis on data security and privacy. Discusses healthcare scaling and resource efficiency considerations. Examines methods for sharing information among various healthcare organizations while retaining model performance. This book is aimed at graduate students and researchers in federated learning, data science, AI/machine learning, and healthcare.
Copyright:
2025

Book Details

Book Quality:
Publisher Quality
ISBN-13:
9781040126141
Related ISBNs:
9781040126127, 9781032689555, 9781032694870
Publisher:
CRC Press
Date of Addition:
Copyrighted By:
selection and editorial matter, Amandeep Kaur, Chetna Kaushal, Md. Mehedi Hassan, and Si Thu Aung
Adult content:
No
Language:
English
Has Image Descriptions:
No
Categories:
Nonfiction, Computers and Internet
Submitted By:
Bookshare Staff
Usage Restrictions:
This is a copyrighted book.
Edited by:
Amandeep Kaur
Edited by:
Chetna Kaushal
Edited by:
Md. Mehedi Hassan
Edited by:
Si Thu Aung

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