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

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