Business & Economics Books:

Federated Learning for Digital Healthcare Systems

Click to share your rating 0 ratings (0.0/5.0 average) Thanks for your vote!

Format:

Paperback / softback
$450.00
Releases

Pre-order to reserve stock from our first shipment. Your credit card will not be charged until your order is ready to ship.

Available for pre-order now
Free Delivery with Primate
Join Now

Free 14 day free trial, cancel anytime.

Buy Now, Pay Later with:

4 payments of $112.50 with Afterpay Learn more

6 weekly interest-free payments of $75.00 with Laybuy Learn more

Pre-order Price Guarantee

If you pre-order an item and the price drops before the release date, you'll pay the lowest price. This happens automatically when you pre-order and pay by credit card or pickup.

If paying by PayPal, Afterpay, Laybuy, Zip, Klarna, POLi, Online EFTPOS or internet banking, and the price drops after you have paid, you can ask for the difference to be refunded.

If Mighty Ape's price changes before release, you'll pay the lowest price.

Availability

This product will be released on

Delivering to:

It should arrive:

  • 7-14 June using International Courier

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.

Author Biography:

Agbotiname Lucky Imoize 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 is a full professor at the Department of Computer Science at King Abdullah II School of Information Technology (KASIT), University of Jordan, Amman, Jordan. He is also the Chair and Professor of the Computer Science Department and Director of the Cybersecurity Center at The University of Texas Permian Basin. 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
Release date NZ
June 1st, 2024
Audience
  • Professional & Vocational
Contributors
  • Edited by Agbotiname Lucky Imoize
  • Edited by Houbing Song
  • Edited by Mohammad S. Obaidat
  • Series edited by Fatos Xhafa
Pages
300
ISBN-13
9780443138973
Product ID
38481765

Customer previews

Nobody has previewed this product yet. You could be the first!

Write a Preview

Help & options

Filed under...