Privacy Meets Profit: Federated Learning in Metaverse Healthcare

Abstract

Healthcare holds almost one-third of the world's data, which has caused worries over data privacy and the system's financial viability, as every security incident costs on average $10.93 million. To ensure the confidentiality of patients' information, the current research proposes a federated learning framework utilizing a metaverse paradigm with token-based incentives as a method for secure, encrypted, and decentralized data collection and processing. The simulation outcomes indicate improved diagnostics, which, in turn, reduces the risk of data leaks. This architecture, in a private first ecosystem, is a great way to create more value by deploying it in a scalable and cost-effective manner. As the AI market in healthcare is anticipated to be worth $ 187 billion by 2030, the proposed model provides a strong foundation for building secure, engaging, and ethically regulated digital health environments that foster longterm ecological trust.

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