Federated Learning for Digital Healthcare Systems (Intelligent Data-Centric Systems) - Softcover

Softcover

ISBN 10: 0443138974 ISBN 13: 9780443138973

Publisher: Academic Press, 2024

Synopsis

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.

• 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

About this title

About the Authors

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

From the Back Cover

Modern healthcare systems facilitate the collection of critical medical data for statistical evaluation and inference using machine learning, however, the application of ML in healthcare data analytics has not been fully exploited due to the proliferation of security and privacy concerns. The potential of machine learning is also limited by insufficient data, posing a significant impediment to the transition from research to clinical practice. Over the past five years, Federated Learning has been introduced to strengthen the performance of machine learning. In federated learning, artificial intelligence models are trained with data from multiple sources. In this case, data anonymity, security, privacy and integrity are maintained, thus removing potential barriers to data sharing. Additionally, models trained by federated learning have shown favorable progress in the agreement with models obtained from centrally hosted data sets. A successfully implemented federated learning model can produce unbiased decisions which facilitate better-informed decision making in precision medicine.

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, 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.

Edition details

• Publisher: Academic Press
• Publication date: 2024
• ISBN 10: 0443138974
• ISBN 13: 9780443138973
• Binding: Paperback
• Language: English
• Number of pages: 458
• Editor: Imoize, Obaidat, Song