Federated Learning in Metaverse Healthcare: Personalized Medicine and Wellness

Author: Mahajan PhD, Shubham

Edition: 1

ISBN: 9780443337895

Release Date: 19-09-2025

Package Dimensions: 9.3 x 7.5 x 0.9 inches

Languages: English

Binding: paperback

Number Of Pages: 388

Details: 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. 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