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

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:

10/02/24

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