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.
1. Federated Learning for Digital Healthcare Systems - p. Page i 2. Cover - p. Page i 3. Title page - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Contents - p. Page vi 7. List of contributors - p. Page vii 8. Preface - p. Page vii 9. List of Illustrations - p. Page xiv 10. List of Tables - p. Page xv 11. Chapter 1 : Digital healthcare systems in a federated learning perspective - p. Page xv 12. Chapter 2 : Architecture and design choices for federated learning in modern digital healthcare systems - p. Page 27 13. Chapter 3 : Curation of federated patient data: a proposed landscape for the African Health Data Space - p. Page 56 14. Chapter 4 : Recent advances in federated learning for digital healthcare systems - p. Page 77 15. Chapter 5 : Performance evaluation of federated learning algorithms using breast cancer dataset - p. Page 92 16. Chapter 6 : Taxonomy for federated learning in digital healthcare systems - p. Page 112 17. Chapter 7 : IoHT-FL model to support remote therapies for children with psychomotor deficit - p. Page 143 18. Chapter 8 : Blockchain-based federated learning in internet of health things - p. Page 173 19. Chapter 9 : Integration of federated learning paradigms into electronic health record systems - p. Page 195 20. Chapter 10 : Technical considerations of federated learning in digital healthcare systems - p. Page 230 21. Chapter 11 : Federated learning challenges and risks in modern digital healthcare systems - p. Page 272 22. Chapter 12 : Case studies and recommendations for designing federated learning models for digital healthcare systems - p. Page 295 23. Chapter 13 : Government and economic regulations on federated learning in emerging digital healthcare systems - p. Page 320 24. Chapter 14 : Legal implications of federated learning integration in digital healthcare systems - p. Page 351 25. Chapter 15 : Secure federated learning in the Internet of Health Things for improved patient privacy and data security - p. Page 380 26. Index - p. Page 403
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.
1. Federated Learning for Digital Healthcare Systems - p. Page i 2. Cover - p. Page i 3. Title page - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Contents - p. Page vi 7. List of contributors - p. Page vii 8. Preface - p. Page vii 9. List of Illustrations - p. Page xiv 10. List of Tables - p. Page xv 11. Chapter 1 : Digital healthcare systems in a federated learning perspective - p. Page xv 12. Chapter 2 : Architecture and design choices for federated learning in modern digital healthcare systems - p. Page 27 13. Chapter 3 : Curation of federated patient data: a proposed landscape for the African Health Data Space - p. Page 56 14. Chapter 4 : Recent advances in federated learning for digital healthcare systems - p. Page 77 15. Chapter 5 : Performance evaluation of federated learning algorithms using breast cancer dataset - p. Page 92 16. Chapter 6 : Taxonomy for federated learning in digital healthcare systems - p. Page 112 17. Chapter 7 : IoHT-FL model to support remote therapies for children with psychomotor deficit - p. Page 143 18. Chapter 8 : Blockchain-based federated learning in internet of health things - p. Page 173 19. Chapter 9 : Integration of federated learning paradigms into electronic health record systems - p. Page 195 20. Chapter 10 : Technical considerations of federated learning in digital healthcare systems - p. Page 230 21. Chapter 11 : Federated learning challenges and risks in modern digital healthcare systems - p. Page 272 22. Chapter 12 : Case studies and recommendations for designing federated learning models for digital healthcare systems - p. Page 295 23. Chapter 13 : Government and economic regulations on federated learning in emerging digital healthcare systems - p. Page 320 24. Chapter 14 : Legal implications of federated learning integration in digital healthcare systems - p. Page 351 25. Chapter 15 : Secure federated learning in the Internet of Health Things for improved patient privacy and data security - p. Page 380 26. Index - p. Page 403
