Federated Learning for Digital Healthcare Systems
Published on 6. June 2024
Book
Paperback/Softback
458 pages
978-0-443-13897-3 (ISBN)
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.
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.
More details
Series
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
Dimensions
Height: 235 mm
Width: 191 mm
Weight
920 gr
ISBN-13
978-0-443-13897-3 (9780443138973)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Agbotiname Lucky Imoize | Mohammad S. Obaidat | Houbing Herbert Song
Federated Learning for Digital Healthcare Systems
E-Book
06/2024
Academic Press
€160.00
Available for download
Persons
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. 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. 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. 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 fatos@cs.upc.edu. Please visit also http://www.cs.upc.edu/~fatos/ and at http://dblp.uni-trier.de/pers/hd/x/Xhafa:Fatos
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 fatos@cs.upc.edu. Please visit also http://www.cs.upc.edu/~fatos/ and at http://dblp.uni-trier.de/pers/hd/x/Xhafa:Fatos
Content
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