Federated Learning in Health Care Technology

Erscheinungsjahr 2025
ISBN: 978-981-968352-9
Verlag: Springer

Buch, Englisch, 234 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 533 g

Reihe: Studies in Computational Intelligence

This book offers an in-depth exploration of federated learning (FL), a groundbreaking approach that facilitates collaborative data analysis while ensuring patient privacy and data security. As healthcare systems worldwide generate vast amounts of data, the challenge lies in harnessing this information without compromising confidentiality. Federated learning addresses this by allowing multiple institutions to collaborate on machine learning models without sharing sensitive data. In this context, the authors delve into the foundational principles of FL, illustrating how it enables the aggregation of decentralized data to improve diagnostic accuracy, develop personalized treatment plans, and enhance overall healthcare outcomes. The authors present real-world applications across various medical fields, from predictive analytics in chronic disease management to precision medicine and beyond. Additionally, the authors discuss the ethical and regulatory landscapes, providing insights into the challenges and solutions associated with implementing FL in healthcare. This book is designed for a diverse audience, including researchers, healthcare practitioners, data scientists, and policymakers. It aims to bridge the gap between cutting-edge technology and practical medical applications, offering a comprehensive guide to leveraging FL for healthcare innovation.

Zielgruppe

Research

Autoren/Hrsg.

Mridha, Muhammad Firoz

Dey, Nilanjan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision

Inhaltsverzeichnis

Federated Learning in Health Care Technology Challenges, Solutions and Opportunities. Decentralized Tumor Classification with Federated Learning A Privacy-Preserving Approach.- Transforming Healthcare Analytics.- Privacy-Preserving Kidney Stone Detection from X-ray Image using Federated Learning. Federated Learning for Privacy-Preserving Healthcare Analytics: A Novel Framework for Fraud Detection in Healthcare.

Über Autor(innen)

M. F. Mridha is currently working as an associate professor in the Department of Computer Science, American International University-Bangladesh (AIUB). He is the founder and director of Advanced Machine Intelligence Research Lab (AMIR Lab). He also worked as an associate professor and chairman in the Department of Computer Science and Engineering, Bangladesh University of Business and Technology (BUBT) from 2019 to 2022 and as a CSE department faculty member at the University of Asia Pacific and as a graduate head from 2012 to 2019. He received his Ph.D. in NLP in the domain of AI from Jahangirnagar University in the year 2017. He has authored/edited several books with Springer and published more than 200 papers. His research work contributed to the reputed Journal of Scientific Reports Nature, Knowledge-Based Systems, Artificial Intelligence Review, Engineering Applications of Artificial Intelligence, IEEE Access, Sensors, Cancers, Biology and Applied Sciences, etc. 
 
Nilanjan Dey is an associate professor in the Department of Computer Science and Engineering, Techno International New Town, Kolkata, India. He is a visiting fellow of the University of Reading, UK. He was an honorary Visiting Scientist at Global Biomedical Technologies Inc., CA, USA (2012–2015). He was awarded his PhD. from Jadavpur University in 2015. He has authored/edited more than 70 books with Elsevier, Wiley, CRC Press and Springer and published more than 300 papers. He is the editor-in-chief of International Journal of Ambient Computing and Intelligence, IGI Global, associated editor of  IEEE Access and International Journal of Information Technology, Springer. He is the series co-editor of Springer Tracts in Nature-Inspired Computing, Springer, series co-editor of Advances in Ubiquitous Sensing Applications for Healthcare, Elsevier, series editor of Computational Intelligence in Engineering Problem Solving and Intelligent Signal processing and data analysis, CRC.