Federated Learning in Metaverse Healthcare
Personalized Medicine and Wellness
Published on 15. September 2025
Book
Paperback/Softback
388 pages
978-0-443-33789-5 (ISBN)
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.
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.
More details
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 234 mm
Width: 191 mm
Thickness: 23 mm
Weight
839 gr
ISBN-13
978-0-443-33789-5 (9780443337895)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Shubham Mahajan | Jyotir Moy Chatterjee
Federated Learning in Metaverse Healthcare
Personalized Medicine and Wellness
E-Book
09/2025
Elsevier
€167.99
Available for download
Persons
Dr. Shubham Mahajan, a distinguished member of prestigious organizations such as IEEE, ACM, and IAENG, boasts an impressive academic and professional background. He earned his B.Tech. degree from Baba Ghulam Shah Badshah University, his M.Tech. degree from Chandigarh University, and his Ph.D. degree from Shri Mata Vaishno Devi University (SMVDU) in Katra, India.
Dr. Mahajan has a remarkable track record in the field of artificial intelligence and image processing, holding an impressive portfolio of eleven Indian patents, as well as one Australian and one German patent. His contributions to the field are further evidenced by his extensive publication record, which includes over 100+ articles published in peer-reviewed journals, conferences and 10+ books. His research interests span a wide array of topics, encompassing image processing, video compression, image segmentation, fuzzy entropy, nature-inspired computing methods, optimization, data mining, machine learning, robotics, and optical communication. Notably, his dedication and expertise have earned him the 'Best Research Paper Award' from ICRIC 2019, published by Springer in the LNEE series.
In recognition of his exceptional achievements, Dr. Mahajan has received numerous accolades and honours throughout his career. These include the Best Student Award in 2019, the IEEE Region-10 Travel Grant Award in 2019, the 2nd runner-up prize in the IEEE RAS HACKATHON in 2019 (held in Bangladesh), the IEEE Student Early Researcher Conference Fund (SERCF) in 2020, the Emerging Scientist Award in 2021, and the IEEE Signal Processing Society Professional Development Grant in 2021. His commitment to excellence in research was further underscored by his receipt of the Excellence in Research Award in 2023.
Dr. Mahajan's impact extends beyond the realm of academia. He has served as a Campus Ambassador for IEEE, representing esteemed institutions such as IIT Bombay, Kanpur, Varanasi, Delhi, as well as various multinational corporations. His active engagement in fostering international research collaborations reflects his enthusiasm for advancing the frontiers of knowledge and innovation on a global scale.
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.
Dr. Mahajan has a remarkable track record in the field of artificial intelligence and image processing, holding an impressive portfolio of eleven Indian patents, as well as one Australian and one German patent. His contributions to the field are further evidenced by his extensive publication record, which includes over 100+ articles published in peer-reviewed journals, conferences and 10+ books. His research interests span a wide array of topics, encompassing image processing, video compression, image segmentation, fuzzy entropy, nature-inspired computing methods, optimization, data mining, machine learning, robotics, and optical communication. Notably, his dedication and expertise have earned him the 'Best Research Paper Award' from ICRIC 2019, published by Springer in the LNEE series.
In recognition of his exceptional achievements, Dr. Mahajan has received numerous accolades and honours throughout his career. These include the Best Student Award in 2019, the IEEE Region-10 Travel Grant Award in 2019, the 2nd runner-up prize in the IEEE RAS HACKATHON in 2019 (held in Bangladesh), the IEEE Student Early Researcher Conference Fund (SERCF) in 2020, the Emerging Scientist Award in 2021, and the IEEE Signal Processing Society Professional Development Grant in 2021. His commitment to excellence in research was further underscored by his receipt of the Excellence in Research Award in 2023.
Dr. Mahajan's impact extends beyond the realm of academia. He has served as a Campus Ambassador for IEEE, representing esteemed institutions such as IIT Bombay, Kanpur, Varanasi, Delhi, as well as various multinational corporations. His active engagement in fostering international research collaborations reflects his enthusiasm for advancing the frontiers of knowledge and innovation on a global scale.
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.
Content
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
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