Federated Deep Leaing 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 leaing for healthcare including fundamental concepts, framework, and the applications comprising domain adaptation, model distillation, and transfer leaing. It covers conces in model faiess, 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 leaing systems that safeguard private medical information.Features: Offers a thorough introduction of federated deep leaing 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 leaing, data science, AI/machine leaing, and healthcare.

About the Author

This book provides a practical guide to federated deep leaing for healthcare including fundamental concepts, framework, and the applications comprising domain adaptation, model distillation, and transfer leaing. It covers conces in model faiess, 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 leaing systems that safeguard private medical information.Features: Offers a thorough introduction of federated deep leaing 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 leaing, data science, AI/machine leaing, and healthcare.