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

Federated Deep Learning for Healthcare: A Practical Guide with Challenges and Opportunities (Advances in Smart Healthcare Technologies)
by: Amandeep Kaur (Editor),Chetna Kaushal (Editor),Md. Mehedi Hassan (Editor),Si Thu Aung (Editor)&1more
Publisher: CRC Press
Edition: 1st
Publication Date: 2024/10/2
Language: English
Print Length: 252 pages
ISBN-10: 1032689552
ISBN-13: 9781032689555
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.
About the Author
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. Read more