Federated Deep Learning for Healthcare

A Practical Guide with Challenges and Opportunities

Amandeep Kaur, Chetna Kaushal, Md. Mehedi Hassan, Si Thu Aung

• Publisher: CRC Press
• Year: 2024
• Print ISBN: 9781032689555
• eBook ISBN: 9781040126141
• Edition: 1
• Topic: Computer Science
• Subtopic: Computer Engineering
• Index: Computer Science

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.

Table of contents

• Cover
• Half Title
• Series Page
• Title Page
• Copyright Page
• Table of Contents
• About the Editors
• List of Contributors
• Chapter 1 Revolutionizing Healthcare through Federated Learning: A Secure and Collaborative Approach
• Chapter 2 Revolutionizing Healthcare: Unleashing the Power of Digital Health
• Chapter 3 Federated Deep Learning Systems in Healthcare
• Chapter 4 Applications of Federated Deep Learning Models in Healthcare Era
• Chapter 5 Machine Learning for Healthcare: Review and Future Aspects
• Chapter 6 Federated Multi-Task Learning to Solve Various Healthcare Challenges
• Chapter 7 Smart System for Development of Cognitive Skills Using Machine Learning
• Chapter 8 Patient-Driven Federated Learning (PD-FL): An Overview
• Chapter 9 An Explainable and Comprehensive Federated Deep Learning in Practical Applications: Real World Benefits and Systematic Analysis Across Diverse Domains
• Chapter 10 Federated Deep Learning System for Application of Healthcare in Pandemic Situation
• Chapter 11 The Integration of Federated Deep Learning with Internet of Things in Healthcare
• Chapter 12 FireEye: An IoT-Based Fire Alarm and Detection System for Enhanced Safety
• Chapter 13 Safeguarding Data Privacy and Security in Federated Learning Systems
• Chapter 14 Diseases Detection System Using Federated Learning
• Chapter 15 Tailoring Medicine through Personalized Healthcare Solutions
• Chapter 16 FedHealth in Wearable Healthcare, Orchestrated Federated Deep Learning for Smart Healthcare: Health Monitoring and Healthcare Informatics Lensing Challenges and Future Directions
• Chapter 17 From Scarce to Abundant: Enhancing Learning with Federated Transfer Techniques
• Chapter 18 Federated Learning-Based AI Approaches for Predicting Stroke
• Index