Federated Learning for Healthcare
Applications with Case Studies
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A termék adatai:
- Kiadás sorszáma 1
- Kiadó Chapman and Hall
- Megjelenés dátuma 2026. április 29.
- ISBN 9781032978109
- Kötéstípus Keménykötés
- Terjedelem296 oldal
- Méret 234x156 mm
- Súly 453 g
- Nyelv angol
- Illusztrációk 70 Illustrations, black & white; 70 Line drawings, black & white; 40 Tables, black & white 700
Kategóriák
Rövid leírás:
The book offers an in-depth exploration of federated learning and its transformative impact on the healthcare industry.
TöbbHosszú leírás:
The book offers an in-depth exploration of federated learning and its transformative impact on the healthcare industry. It begins by introducing the foundational concepts of federated learning, including its methods and applications within various healthcare domains. It explores how federated learning allows for model training using decentralised data, such as patient records, medical imaging, and wearable sensor data, without centralising sensitive information. This approach ensures patient privacy and addresses critical challenges in healthcare data management.
• A detailed overview of federated learning, its principles, and its relevance to the healthcare sector.
• Insights into how federated learning enhances clinical decision-making, disease prediction, diagnosis, and personalised treatment through decentralised data sources.
• Examination of issues such as communication overhead, model heterogeneity, and data distribution imbalance, with strategies to overcome these challenges.
• Practical examples of successful federated learning implementations in healthcare demonstrate its impact on patient care and operational efficiency.
• Discussions on maintaining data privacy, ensuring compliance with regulations, and addressing ethical concerns.
This book is for researchers, healthcare professionals, data scientists, and policymakers interested in leveraging federated learning to enhance healthcare.
TöbbTartalomjegyzék:
Chapter 1 Framework of Federated Learning and Implementation Through Internet of Things Chapter 2 Federated Learning for Internet of Things Chapter 3 Enhancing Federated Learning in Healthcare using Adaptive Genetic Model Aggregation Chapter 4 Energy Efficiency Iot Devices in Healthcare Field Using Federated
Learning Chapter 5 Artificial Intelligence in Mental Health: Innovation, Ethics, and Privacy via Federated Learning Chapter 6 Adaptive Energy Management in Healthcare IoT Devices Using Federated Intelligence Chapter 7 Artificial Intelligence Based Federated Learning in Healthcare Chapter 8 Federated Learning for Diabetic Retinopathy: Enhancing Detection with Data Privacy Chapter 9 Federated Learning Framework For Motif Structure Prediction Chapter 10 Cyber Threat Detection in IoT-Enabled Edge Computing Using Optimized Federated Deep Learning Chapter 11 Optimized Migraine Detection in Healthcare: Exploring Random
Forest and XGBoost with Prospects for Federated Learning Chapter 12 Unlocking the Knowledge of Patient Similarities in Chronic Kidney
Disease CKD Using Machine Learning with Prospects of Federal Learning Chapter 13 Dynamic Pricing for Revenue Management in Health and Hospitality
Industry with Federated Learning Chapter 14 Federated Learning for Real-Time Disease Prediction: A Scalable Framework for Personalized Healthcare in Internet of Things enabled Environment
