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.
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End Time: 2024-12-02T07:43:13.000Z
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EAN: 9781032689555
UPC: 9781032689555
ISBN: 9781032689555
MPN: N/A
Format: Hardback, 252 pages
Author: Amandeep Kaur (Edited by)
Book Title: Federated Deep Learning for Healthcare: A Practica
Item Height: 1.6 cm
Item Length: 23.4 cm
Item Weight: 0.55 kg
Item Width: 15.6 cm
Language: Eng
Publisher: CRC Press
