Textbook in PDF format

This book provides a practical guide to Federated Deep Learning for healthcare including fundamental concepts, framework, and the applications comprising of 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 like 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.
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