Privacy Preservation Through Design-Federated Learning Architectures for Secure and Immersive Metaverse-Based Healthcare

Crossover of the metaverse, Artificial Intelligence, and Federated Learning is changing the healthcare systems and amplifying the issues of privacy, security, and trust. This chapter discusses privacy preservation by design as a fundamental concept to applying Federated Learning architectures in metaverse-based healthcare setting. Federated Learning is theoretically understood as an architecture facilitator that introduces collaborative intelligence between hospitals, clinics, wearables, and virtual care platforms with no centralized data exchange. The main architectural aspects like Edge Fog Cloud Coordination, Secure Aggregation, Differential Privacy, and Trusted Execution Environment are examined to explain how confidentiality and regulatory compliance may be obtained. By conceptually discussing and using illustrative examples, the chapter demonstrates how privacy-centric federated architectures can be used to support immersive, real-time, and patient-centric healthcare services and maintain data sovereignty.