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Federated Learning in Metaverse Healthcare
Personalized Medicine and Wellness
- 1st Edition - August 4, 2025
- Editors: Shubham Mahajan, Jyotir Moy Chatterjee
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 3 7 8 9 - 5
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 3 7 9 0 - 1
Federated Learning in Metaverse Healthcare: Personalized Medicine and Wellness explores the integration of the metaverse with healthcare, offering immersive experiences and person… Read more
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Request a sales quoteFederated 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.
- Detailed explanations of privacy-preserving techniques in federated learning, such as federated averaging, differential privacy, and secure aggregation, ensuring the protection of sensitive healthcare data
- Presents use cases and case studies demonstrating the practical applications of federated learning in virtual healthcare settings, illustrating its impact on patient care, medical research, and healthcare innovation
- 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
Graduate students, researchers, and professionals in academia and industry who specialize in computer science, federated learning, biomedical engineering, and digital healthcare
- Virtual Clinics and Hospitals: Transforming Healthcare in the Digital Age
- Navigating the Virtual Frontier: Challenges and Solutions for Ethical Federated Learning in Metaverse Healthcare in India
- Review of Deep Reinforcement Learning and Artificial Neural Networks in Healthcare Metaverse
- Virtual Clinical and Hospital in India
- Introduction to Metaverse Healthcare
- Telemedicine in the Metaverse
- Virtual Clinics and Healthcare Ecosystem
- A Collaborative Federated Learning Approach for Healthcare Informatics: Solutions & Challenges
- Privacy and Profit: The Dual Benefits of Federated Learning in Metaverse Healthcare Systems
- The Metaverse Shift: Adapting to Decentralized Computing in Federated Learning for Healthcare
- Federated Learning for Predictive Modeling of Disease Prevention in the Metaverse
- Integrating Real-Time Data with Predictive Models for Early Disease Detection in Metaverse Healthcare
- Augmented Reality Wearables for Health Monitoring in the Metaverse: Enhancing Patient Engagement and Clinical Outcomes
- Adapting to Decentralization: The Evolution of Computing Paradigms and Machine Learning in Federated Learning
- Privacy-Preserving Secure Computation: Bridging Traditional Healthcare and Metaverse Telemedicine
- Breaking the Boundaries: Optimizing Healthcare in the Metaverse through Federated Learning
- No. of pages: 300
- Language: English
- Edition: 1
- Published: August 4, 2025
- Imprint: Academic Press
- Paperback ISBN: 9780443337895
- eBook ISBN: 9780443337901
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Shubham Mahajan
Dr. Shubham Mahajan is a distinguished academic and professional member of prestigious organizations such as IEEE, ACM, and IAENG. He earned his B.Tech. from Baba Ghulam Shah Badshah University, his M.Tech. from Chandigarh University, his Ph.D. from Shri Mata Vaishno Devi University in India, and his Postdoctoral degree in Applied Data Science at Noroff University College in Norway. Currently, he is working as an Assistant Professor at Amity University, Haryana, India.
Dr. Mahajan specializes in artificial intelligence, image processing and segmentation, data mining, and machine learning, holding eleven Indian patents along with one patent each from Australia and Germany.
Affiliations and expertise
Assistant Professor, Amity School of Engineering and Technology (ASET) Amity University, IndiaJM
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