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.Brief description: Agbotiname Lucky Imoize is currently a Marie Sklodowska-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.