Federated Learning for Digital Healthcare Systems critically examines the key factors that contribute to the problem of applying machine learning in healthcare systems and investigates how federated learning can be employed to address the problem. The book discusses, examines, and compares the applications of federated learning solutions in emerging digital healthcare systems, providing a critical look in terms of the required resources, computational complexity, and system performance.
In the first section, chapters examine how to address critical security and privacy concerns and how to revamp existing machine learning models. In subsequent chapters, the book's authors review recent advances to tackle emerging efficient and lightweight algorithms and protocols to reduce computational overheads and communication costs in wireless healthcare systems. Consideration is also given to government and economic regulations as well as legal considerations when federated learning is applied to digital healthcare systems.
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1. Digital Healthcare Systems in a Federated Learning Perspective
2. Architecture and design choices for federated learning in modern digital healthcare systems
3. Curation of Federated Patient Data: A Proposed Landscape for the Africa Health Data Space
4. Recent advances in federated learning for digital healthcare systems
5. Performance evaluation of federated learning algorithms using a breast cancer dataset
6. Taxonomy for federated learning applied to digital healthcare systems
7. Modeling an Internet of Health Things Using Federated Learning to Support Remote Therapies for Children with Psychomotor Deficit
8. Blockchain-Based Federated Learning in Internet of Health Things (IoHT)
9. Integration of Federated Learning Paradigms into Electronic Health Record Systems
10. Technical considerations of federated learning in digital healthcare systems
11. Federated Learning Challenges and Risks in Modern Digital Healthcare Systems
12. Case studies and recommendations for designing federated learning models for digital healthcare systems
13. Government and economic regulations on federated learning in emerging digital healthcare systems
14. Legal implications of federated learning in emerging digital healthcare systems
15. Secure Federated Learning in the Internet of Health Things (IoHT) for Improved Patient Privacy and Data Security
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Explores the potential of federated learning in emerging digital healthcare systems
Provides insights into real-world scenarios of the design, development, deployment, application, management, and benefits of federated learning in emerging digital healthcare systems
Highlights the need to design efficient federated learning-based algorithms to tackle the proliferating security and patient privacy issues in digital healthcare systems
Reviews the latest research, along with practical solutions and applications developed by global experts from academia and industry
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Produktdetaljer
ISBN
9780443138973
Publisert
2024-06-06
Utgiver
Vendor
Academic Press Inc
Vekt
450 gr
Høyde
235 mm
Bredde
191 mm
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
458
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