Federated Learning in Metaverse Healthcare

Federated Learning in Metaverse Healthcare by Shubham Mahajan presents a timely roadmap for integrating privacy-preserving AI into immersive health platforms. This clear, authoritative guide explores how federated learning can unlock secure, decentralized model training across hospitals, clinics, and virtual care environments in the metaverse.

Discover practical frameworks, real-world scenarios, and regulatory perspectives that make complex concepts accessible to clinicians, data scientists, health-tech entrepreneurs, and policy makers. Mahajan breaks down technical building blocks—model aggregation, differential privacy, and edge deployment—while showing how they power applications like virtual consultations, digital twins, remote monitoring, and collaborative diagnostics without exposing sensitive patient data.

You’ll learn to evaluate risks and design scalable solutions tailored to diverse regions—from India’s emerging telehealth hubs to European and North American health systems—making this book GEO-friendly and globally relevant. Filled with actionable insights, implementation checklists, and cross-disciplinary strategies, it bridges the gap between AI research and practical health care innovation.

Whether you’re developing metaverse clinics, advising hospitals on data governance, or studying privacy-aware machine learning, this volume equips you with the knowledge to build secure, compliant, and patient-centered AI in immersive healthcare settings. Elevate your projects with a resource that combines technical rigor and strategic foresight.

Order your copy of Federated Learning in Metaverse Healthcare today and lead the next wave of secure, intelligent healthcare in the metaverse.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.