Federated Learning for Digital Healthcare Systems

1st Edition - June 2, 2024
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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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.

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

Agbotiname Lucky Imoize

Agbotiname Lucky Imoize is al Lecturer in the Department of Electrical and Electronics Engineering at the University of Lagos, Nigeria. He was awarded the Fulbright Fellowship as a Visiting Research Scholar at the Wireless@VT Lab in the Bradley Department of Electrical and Computer Engineering, Virginia Tech, USA. He is currently a research scholar at Ruhr University Bochum, Germany, under the Nigerian Petroleum Technology Development Fund (PTDF) and the German Academic Exchange Service (DAAD) through the Nigerian-German Postgraduate Program. He is a Registered Engineer with the Council for the Regulation of Engineering in Nigeria (COREN) and a Nigerian Society of Engineers (NSE) member. He has co-edited two books and coauthored over 90 wireless communication papers in peer-reviewed journals. His research interests are 6G wireless communication, Artificial Intelligence, and the Internet of Things.

Affiliations and expertise

Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Lagos, Nigeria

Fatos Xhafa

Fatos Xhafa, PhD in Computer Science, is Full Professor at the Technical University of Catalonia (UPC), Barcelona, Spain. He has held various tenured and visiting professorship positions. He was a Visiting Professor at the University of Surrey, UK (2019/2020), Visiting Professor at the Birkbeck College, University of London, UK (2009/2010) and a Research Associate at Drexel University, Philadelphia, USA (2004/2005). He was a Distinguished Guest Professor at Hubei University of Technology, China, for the duration of three years (2016-2019). Prof. Xhafa has widely published in peer reviewed international journals, conferences/workshops, book chapters, edited books and proceedings in the field (H-index 55). He has been awarded teaching and research merits by the Spanish Ministry of Science and Education, by IEEE conferences and best paper awards. Prof. Xhafa has an extensive editorial service. He is founder and Editor-In-Chief of Internet of Things - Journal - Elsevier (Scopus and Clarivate WoS Science Citation Index) and of International Journal of Grid and Utility Computing, (Emerging Sources Citation Index), and AE/EB Member of several indexed Int'l Journals. Prof. Xhafa is a member of IEEE Communications Society, IEEE Systems, Man & Cybernetics Society and Founder Member of Emerging Technical Subcommittee of Internet of Things. His research interests include IoT and Cloud-to-thing continuum computing, massive data processing and collective intelligence, optimization, security and trustworthy computing and machine learning, among others. He can be reached at [email protected]. Please visit also http://www.cs.upc.edu/~fatos/ and at http://dblp.uni-trier.de/pers/hd/x/Xhafa:Fatos

Affiliations and expertise

Full Professor of Computer Science, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain

Mohammad S. Obaidat

Mohammad S. Obaidat (Fellow IEEE, Fellow SCS, Fellow AAIA, and Fellow of FTRA) is a Full Professor in the Department of Computer Science at King Abdullah II School of Information Technology (KASIT), University of Jordan, Amman, Jordan. He is also a Distinguished Professor at SRM University, India. He has published around 1,200 technical articles, 100 books, and 70 book chapters. He is Editor-in-Chief of three scholarly journals and an editor of numerous international journals. He is the founding Editor-in-Chief of Wiley Security and Privacy Journal. Moreover, he is the founder or co-founder of five International Conferences. His areas of interest are in wireless networks, cyber security, security of e-Systems, computer networks, data analytics, and computer architecture, parallel computing: Architecture, and Algorithms, and performance evaluation of computer networks and systems.

Affiliations and expertise

University of Jordan, Aman, Jordan

Houbing Herbert Song

Houbing Song, Security and Optimization for Networked Globe Laboratory, University of Maryland, Baltimore County (UMBC), Baltimore, USA. His research interests include cyber-physical systems, cybersecurity and privacy, IoT, big data analytics, connected vehicles, smart health, wireless communications, and networking. Dr. Song has edited and authored several books in the field, including Cyber-Physical Systems: Foundations, Principles and Applications.

Affiliations and expertise

University of Maryland, Baltimore County (UMBC), Baltimore, USA

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Federated Learning for Digital Healthcare Systems

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Agbotiname Lucky Imoize

Fatos Xhafa

Mohammad S. Obaidat

Houbing Herbert Song

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