Federated Learning for Digital Healthcare Systems
- 1st Edition - June 2, 2024
- Latest edition
- Editors: Agbotiname Lucky Imoize, Fatos Xhafa, Mohammad S. Obaidat, Houbing Herbert Song
- Language: English
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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Description
Description
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.
Key features
Key features
- 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
Readership
Readership
Graduate students, researchers, and professionals from academia and industry, working in the fields of computer science, federated learning and digital healthcare
Table of contents
Table of contents
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
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
Product details
Product details
- Edition: 1
- Latest edition
- Published: June 2, 2024
- Language: English
About the editors
About the editors
AI
Agbotiname Lucky Imoize
Agbotiname Lucky Imoize is currently a Marie Skłodowska‑Curie Fellow affiliated with the Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT) and Politecnico di Torino, Italy. Dr. Imoize has held international research appointments, including as a research scholar at Ruhr University Bochum, Germany, supported by the Nigerian Petroleum Technology Development Fund (PTDF) and the German Academic Exchange Service (DAAD), and as a Fulbright Visiting Researcher at the Wireless@VT Laboratory in the Bradley Department of Electrical and Computer Engineering, Virginia Tech, USA. His industry experience includes roles at ZTE and Globacom. He is Vice Chair of the IEEE Communications Society Nigeria Chapter and a registered engineer with the Council for the Regulation of Engineering in Nigeria. His research interests include AI, IoT, deep learning, and wireless communications for sustainable development applications.
Affiliations and expertise
Researcher, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy; Marie Skodowska-Curie Fellow, Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT), Italy; Lecturer, Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Lagos, NigeriaFX
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, SpainMO
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, JordanHS
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, USAView book on ScienceDirect
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