Federated Learning in Metaverse Healthcare: Personalized Medicine and Wellness

Jyotir Moy Chatterjee · Shubham Mahajan

2025년 11월 · Academic Press

eBook

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eBook 정보

"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

저자 정보

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, M.Tech. from Chandigarh University, and Ph.D. from Shri Mata Vaishno Devi University in India. Currently, he is pursuing a Post Doctoral degree in Applied Data Science at Noroff University College in Norway and serves as a Visiting Assistant Professor at Al-Ahliyya Amman University in Jordan. Dr. Mahajan specializes in artificial intelligence and image processing, holding eleven Indian patents along with one each from Australia and Germany. His extensive publication record includes over 70 articles in peer-reviewed journals and conferences, covering topics such as image segmentation, data mining, and machine learning. He received the 'Best Research Paper Award' from ICRIC 2019, published in the Springer LNEE series. Throughout his career, Dr. Mahajan has earned numerous accolades, including the Best Student Award and the Emerging Scientist Award. He has also actively represented IEEE as a Campus Ambassador and has fostered international research collaborations. His commitment to advancing knowledge and innovation in his field is evident through his various contributions and achievements.