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.
- 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
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Techn.
Dateigröße
ISBN-13
978-0-443-13896-6 (9780443138966)
Schweitzer Klassifikation
1. Digital Healthcare Systems in a Federated Learning Perspective2. Architecture and design choices for federated learning in modern digital healthcare systems3. Curation of Federated Patient Data: A Proposed Landscape for the Africa Health Data Space4. Recent advances in federated learning for digital healthcare systems5. Performance evaluation of federated learning algorithms using a breast cancer dataset6. Taxonomy for federated learning applied to digital healthcare systems7. Modeling an Internet of Health Things Using Federated Learning to Support Remote Therapies for Children with Psychomotor Deficit8. Blockchain-Based Federated Learning in Internet of Health Things (IoHT)9. Integration of Federated Learning Paradigms into Electronic Health Record Systems10. Technical considerations of federated learning in digital healthcare systems11. Federated Learning Challenges and Risks in Modern Digital Healthcare Systems12. Case studies and recommendations for designing federated learning models for digital healthcare systems13. Government and economic regulations on federated learning in emerging digital healthcare systems14. Legal implications of federated learning in emerging digital healthcare systems15. Secure Federated Learning in the Internet of Health Things (IoHT) for Improved Patient Privacy and Data Security
