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books
| book details |
Federated Deep Learning for Healthcare: A Practical Guide with Challenges and Opportunities
Edited by Amandeep Kaur, Edited by Chetna Kaushal, Edited by Md. Mehedi Hassan, Edited by Si Thu Aung
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| on special |
normal price: R 2 950.95
Price: R 2 802.95
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| book description |
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.
| product details |
Normally shipped |
Publisher | Taylor & Francis Ltd
Published date | 19 Jul 2026
Language |
Format | Paperback / softback
Pages | 252
Dimensions | 234 x 156 x 0mm (L x W x H)
Weight | 490g
ISBN | 978-1-0326-9486-3
Readership Age |
BISAC |
| other options |
Normally shipped |
Readership Age |
Normal Price | R 2 445.95
Price | R 2 322.95
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