
Federated Learning for Digital Healthcare Systems
- 1st Edition - June 2, 2024
- Imprint: Academic Press
- Editors: Agbotiname Lucky Imoize, Fatos Xhafa, Mohammad S. Obaidat, Houbing Herbert Song
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 3 8 9 7 - 3
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 3 8 9 6 - 6
Federated Learning for Digital Healthcare Systems critically examines the key factors that contribute to the problem of applying machine learning in healthcare systems and inves… Read more

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Request a sales quoteFederated 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
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- Preface
- Chapter 1. Digital healthcare systems in a federated learning perspective
- Abstract
- 1.1 Introduction
- 1.2 Federated learning
- 1.3 Federated learning data processing: as still, stream, and multistream
- 1.4 Applications
- 1.5 Healthcare
- 1.6 Other applications
- 1.7 Federated learning in healthcare ecosystem
- 1.8 Federated learning open research questions
- 1.9 Federated learning challenges in smart healthcare
- 1.10 Future federated learning trends
- 1.11 Conclusion
- Abbreviations
- References
- Further reading
- Chapter 2. Architecture and design choices for federated learning in modern digital healthcare systems
- Abstract
- 2.1 Introduction
- 2.2 Dataspaces and health domain
- 2.3 Proposed approach
- 2.4 Dataspaces and participation in ecosystems
- 2.5 Lessons learned: conclusions and future scope
- Acknowledgment
- References
- Chapter 3. Curation of federated patient data: a proposed landscape for the African Health Data Space
- Abstract
- 3.1 Introduction
- 3.2 Background
- 3.3 Conceptual framework
- 3.4 Methodology
- 3.5 Results: use cases
- 3.6 Landscape for an African Health Data Space
- 3.7 Discussion
- 3.8 Conclusion
- 3.9 Ethical clearance
- Acknowledgments
- References
- Chapter 4. Recent advances in federated learning for digital healthcare systems
- Abstract
- 4.1 Introduction
- 4.2 Related works
- 4.3 Perceptions of federated digital platforms and their use in healthcare
- 4.4 Privacy preservation, security, and ethical needs
- 4.5 Privacy and security Needs
- 4.6 Ethical needs
- 4.7 Role of federated learning in future digital Healthcare 5.0
- 4.8 Federated learning and blockchain for healthcare
- 4.9 Federated learning for collaborative robotics in healthcare
- 4.10 Federated learning for integration with 6G in healthcare
- 4.11 Conclusion
- Acknowledgment
- References
- Chapter 5. Performance evaluation of federated learning algorithms using breast cancer dataset
- Abstract
- 5.1 Introduction
- 5.2 Related works/literature reviews
- 5.3 Federated learning
- 5.4 Inspiration for federated learning
- 5.5 Federated learning algorithm
- 5.6 Materials and methods/methodology/design
- 5.7 Transfer learning
- 5.8 Pretrained classifiers: a short overview
- 5.9 Visual geometry group network
- 5.10 Convolutional neural network model for federated learning
- 5.11 Model evaluation
- 5.12 Results and analysis
- 5.13 Conclusion and future scope
- References
- Chapter 6. Taxonomy for federated learning in digital healthcare systems
- Abstract
- 6.1 Introduction
- 6.2 Related works
- 6.3 Fundamentals of federated learning
- 6.4 Taxonomy of critical aspects of federated learning in healthcare systems
- 6.5 Conclusion and future scope
- References
- Chapter 7. IoHT-FL model to support remote therapies for children with psychomotor deficit
- Abstract
- 7.1 Introduction
- 7.2 Background
- 7.3 Related works
- 7.4 Problem outline
- 7.5 Federated learning architectural model with Internet of Health Things
- 7.6 Real-world scenario
- 7.7 Results
- 7.8 Discussion
- 7.9 Conclusions
- References
- Chapter 8. Blockchain-based federated learning in internet of health things
- Abstract
- 8.1 Introduction
- 8.2 Background
- 8.3 Proposed framework
- 8.4 Internet of Health Things secure and source-aware context design
- 8.5 Implementation
- 8.6 Testimonies and discussions
- 8.7 Conclusion and future prospects
- References
- Further reading
- Chapter 9. Integration of federated learning paradigms into electronic health record systems
- Abstract
- 9.1 Introduction
- 9.2 Federated learning for healthcare
- 9.3 Foundational background of electronic health records
- 9.4 Federated learning-based electronic health records
- 9.5 Industrial use cases of federated learning implementation in electronic health record systems
- 9.6 Open research issues
- 9.7 Conclusion
- References
- Chapter 10. Technical considerations of federated learning in digital healthcare systems
- Abstract
- 10.1 Introduction
- 10.2 Overview of federated learning
- 10.3 Technical considerations of federated learning algorithms in healthcare systems
- 10.4 Technical challenges in executing federated learning models in real-world applications
- 10.5 Proposed solutions to federated learning technical considerations and challenges
- 10.6 Lessons learned
- 10.7 Conclusion and recommendations
- Acknowledgment
- References
- Chapter 11. Federated learning challenges and risks in modern digital healthcare systems
- Abstract
- 11.1 Introduction
- 11.2 Federated learning risks and challenges in modern digital healthcare systems
- 11.3 Lessons
- 11.4 Future research directions
- 11.5 Conclusion
- References
- Chapter 12. Case studies and recommendations for designing federated learning models for digital healthcare systems
- Abstract
- 12.1 Introduction
- 12.2 Federated learning implementation: challenges and solutions
- 12.3 Case studies of federated learning frameworks
- 12.4 Use cases of federated learning in digital healthcare
- 12.5 Open Research Directions
- 12.6 Summary
- 12.7 Conclusions
- References
- Chapter 13. Government and economic regulations on federated learning in emerging digital healthcare systems
- Abstract
- 13.1 Introduction
- 13.2 Related works
- 13.3 Emerging digital healthcare systems
- 13.4 Federated learning in emerging digital healthcare systems
- 13.5 Security and privacy issues in healthcare ecosystem
- 13.6 Regulatory policies on federated learning in emerging digital healthcare systems
- 13.7 Market potential and investment opportunities for federated learning in emerging digital healthcare systems
- 13.8 Commercialization and cost–benefit analysis of fl in emerging digital healthcare systems
- 13.9 Role of federated learning in future digital healthcare systems
- 13.10 Limitations of the study
- 13.11 Conclusion and future works
- References
- Chapter 14. Legal implications of federated learning integration in digital healthcare systems
- Abstract
- 14.1 Introduction
- 14.2 Federated learning integration in healthcare systems
- 14.3 Legal guidelines for healthcare data security and privacy in federated learning setting
- 14.4 Legal considerations for healthcare data protection in federated learning setting
- 14.5 Legal and regulatory procedures in selected jurisdictions
- 14.6 Necessity of legal frameworks for federated learning integration
- 14.7 Conclusions and future scope
- Acknowledgment
- References
- Chapter 15. Secure federated learning in the Internet of Health Things for improved patient privacy and data security
- Abstract
- 15.1 Introduction
- 15.2 Security and privacy challenges of federated learning in Internet of Health Things
- 15.3 Privacy-preserving techniques in federated learning for Internet of Health Things
- 15.4 Lessons
- 15.5 Future research directions on secure federated learning in modern digital healthcare systems
- 15.6 Conclusion
- References
- Index
- Edition: 1
- Published: June 2, 2024
- Imprint: Academic Press
- No. of pages: 458
- Language: English
- Paperback ISBN: 9780443138973
- eBook ISBN: 9780443138966
AI
Agbotiname Lucky Imoize
FX
Fatos Xhafa
MO
Mohammad S. Obaidat
HS