Federated Learning in Metaverse Healthcare: Personalized Medicine and Wellness - Softcover

Softcover

ISBN 10: 0443337896 ISBN 13: 9780443337895

Publisher: Academic Press, 2025

Synopsis

Federated Learning in Metaverse Healthcare: Personalized Medicine and Wellness explores the integration of the metaverse with healthcare, offering immersive experiences and personalized care. The book introduces federated learning, emphasizing its advantages over traditional centralized machine learning in healthcare. It provides a historical context and discusses the technological advancements that led to the emergence of metaverse healthcare. Privacy-preserving methods crucial for protecting sensitive healthcare data within federated learning environments are also examined, underscoring the importance of secure communication protocols. Other important points include the transformation of healthcare delivery through virtual environments, remote consultations, and immersive experiences.

The role of telemedicine in facilitating remote diagnostics and consultations via virtual platforms, and the applications of augmented reality wearables for real-time health monitoring and wellness tracking are detailed. Additionally, the book discusses federated learning's ability to deliver personalized treatment plans tailored to individual patient needs, its role in predictive modeling for disease risks and prevention, as well as virtual health coaches offering personalized guidance for wellness management. The challenges and ethical dilemmas of metaverse healthcare and federated learning, along with potential solutions, are also considered.

• Explains privacy-preserving techniques in federated learning, such as federated averaging, differential privacy, and secure aggregation, thus ensuring the protection of sensitive healthcare data
• Presents use cases and case studies that demonstrate the practical applications of federated learning in virtual healthcare settings
• Illustrates its impact on patient care, medical research, and healthcare innovation
• Contains contributions from leading experts in the fields of healthcare, artificial intelligence, and virtual reality, providing valuable insights and perspectives on the intersection of federated learning and metaverse healthcare

About this title

About the Authors

Dr. Shubham Mahajan is a respected academic and researcher, and a member of well-known professional organizations like IEEE, ACM, and IAENG. He completed his B.Tech. from Baba Ghulam Shah Badshah University, his M.Tech. from Chandigarh University, and his Ph.D. from Shri Mata Vaishno Devi University (SMVDU), Katra. He is currently working as an Assistant Professor at Amity University, Haryana.

Dr. Mahajan has made strong contributions in the fields of artificial intelligence and image processing. He holds nineteen Indian patents, along with one Australian and one German patent. He has published more than 103 research papers in national and international journals and conferences, including 55 in SCIE journals and 48 indexed by Scopus. He has also edited 10 Scopus-indexed books. His research areas include image processing, video compression, image segmentation, fuzzy entropy, nature-inspired algorithms, optimization, data mining, machine learning, robotics, and optical communication. He won the Best Research Paper Award at ICRIC 2019 (Springer LNEE series).

Throughout his career, Dr. Mahajan has received many awards, such as the Best Student Award (2019), IEEE Region-10 Travel Grant (2019), 2nd runner-up in IEEE RAS Hackathon (2019, Bangladesh), IEEE SERCF (2020), Emerging Scientist Award (2021), and the IEEE SPS Professional Development Grant (2021). He also received the Excellence in Research Award in 2023.

Beyond research, Dr. Mahajan has contributed to the academic community in many ways. He has worked as a Campus Ambassador for IEEE at top institutions like IIT Bombay, IIT Kanpur, IIT Varanasi, and IIT Delhi, as well as for several multinational companies. He is active in promoting international research collaborations and serves on Technical Program Committees and Editorial Boards of various international conferences and journals.

Jyotir Moy Chatterjee is currently an Assistant Professor in Department of Computer Science and Engineering at Graphic Era (Deemed to be University), in Dehradun, India. He also serves as a Visiting Faculty member in Information Technology at Lord Buddha Education Foundation, which is affiliated with the Asia Pacific University of Technology & Innovation in Kathmandu, Nepal. His research interests focus on advancements in Machine Learning and Deep Learning.

From the Back Cover

Federated Learning in Metaverse Healthcare: Personalized Medicine and Wellness explores the integration of the metaverse with healthcare, offering immersive experiences and personalized care. It introduces federated learning, highlighting its advantages over centralized machine learning in healthcare. The historical context and technological advancements that have led to the emergence of metaverse healthcare are explored, along with privacy-preserving methods crucial for protecting sensitive healthcare data in federated learning environments. The transformation of healthcare delivery through virtual environments, remote consultations, and immersive experiences is discussed, as well as the role of telemedicine in facilitating remote diagnostics and consultations through virtual platforms. The applications of augmented reality wearables in real-time health monitoring and wellness tracking are explored. The architecture and components of federated learning systems within metaverse healthcare environments are detailed, emphasizing the importance of secure communication protocols in safeguarding healthcare data. Federated learning's ability to deliver personalized treatment plans tailored to individual patient needs, as well as its role in predictive modeling for disease risks and prevention strategies, is examined. Virtual health coaches leveraging federated learning algorithms to provide personalized guidance and support for wellness management are also discussed. The challenges and ethical dilemmas inherent in metaverse healthcare and federated learning are considered, along with potential solutions. Finally, the future of metaverse healthcare and federated learning is speculated, highlighting emerging trends and areas for further research and development.

Edition details

• Publisher: Academic Press
• Publication date: 2025
• Language: English
• ISBN 10: 0443337896
• ISBN 13: 9780443337895
• Binding: Paperback
• Number of pages: 388
• Editor: Mahajan