Skip to main content
Elsevier

Federated Learning in Metaverse Healthcare

Personalized Medicine and Wellness

  • 1st Edition - September 12, 2025
  • Latest edition
  • Editors: Shubham Mahajan, Jyotir Moy Chatterjee
  • Language: English

Federated Learning in Metaverse Healthcare: Personalized Medicine and Wellness explores the integration of the metaverse with healthcare, offering immersive experiences and person… Read more

Description

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.

Key features

  • 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

Readership

Graduate students, researchers, and professionals in academia and industry who specialize in computer science, federated learning, biomedical engineering, and digital healthcare

Table of contents

  1. Virtual Clinics and Hospitals: Transforming Healthcare in the Digital Age
  2. Navigating the Virtual Frontier: Challenges and Solutions for Ethical Federated Learning in Metaverse Healthcare in India
  3. Review of Deep Reinforcement Learning and Artificial Neural Networks in Healthcare Metaverse
  4. Virtual Clinical and Hospital in India
  5. Introduction to Metaverse Healthcare
  6. Telemedicine in the Metaverse
  7. Virtual Clinics and Healthcare Ecosystem
  8. A Collaborative Federated Learning Approach for Healthcare Informatics: Solutions & Challenges
  9. Privacy and Profit: The Dual Benefits of Federated Learning in Metaverse Healthcare Systems
  10. The Metaverse Shift: Adapting to Decentralized Computing in Federated Learning for Healthcare
  11. Federated Learning for Predictive Modeling of Disease Prevention in the Metaverse
  12. Integrating Real-Time Data with Predictive Models for Early Disease Detection in Metaverse Healthcare
  13. Augmented Reality Wearables for Health Monitoring in the Metaverse: Enhancing Patient Engagement and Clinical Outcomes
  14. Adapting to Decentralization: The Evolution of Computing Paradigms and Machine Learning in Federated Learning
  15. Privacy-Preserving Secure Computation: Bridging Traditional Healthcare and Metaverse Telemedicine
  16. Breaking the Boundaries: Optimizing Healthcare in the Metaverse through Federated Learning

Product details

  • Edition: 1
  • Latest edition
  • Published: September 15, 2025
  • Language: English

About the editors

SM

Shubham Mahajan

Dr. Shubham Mahajan is an academic and researcher, member of IEEE, ACM, and IAENG. He earned a B.Tech from Baba Ghulam Shah Badshah University, an M.Tech from Chandigarh University, and a PhD from Shri Mata Vaishno Devi University. He is currently Assistant Professor at Amity University, Haryana. His research spans artificial intelligence and image processing, including video compression, image segmentation, fuzzy entropy, nature-inspired optimization, data mining, machine learning, robotics, and optical communications. He holds patents internationally and has published widely in high-impact venues; he has edited several Scopus-indexed books. He has received multiple awards for research excellence and travel support from IEEE, among others. He has served as IEEE Campus Ambassador at premier institutes and promotes international collaborations. He participates in technical program committees and editorial boards for conferences and journals, shaping discourse in AI and image processing.

Affiliations and expertise
Amity School of Engineering and Technology, Amity University Haryana., India

JM

Jyotir Moy Chatterjee

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.

Affiliations and expertise
Assistant Professor, Department of CSE, Graphic Era University, Dehradun, India

View book on ScienceDirect

Read Federated Learning in Metaverse Healthcare on ScienceDirect