DSFL: A Decentralized SplitFed Learning Approach for Healthcare Consumers in the Metaverse

Abstract

The consumer health industry has witnessed a transformative revolution driven by the Internet of Medical Things (IoMT) and the Metaverse. Additionally, Artificial Intelligence (AI) further enhances this evolution by unlocking the potential for data-driven insights. Yet, there is an imperative requirement to safeguard consumer healthcare data, particularly with the challenges posed by resource-constrained consumer healthcare devices and the complex, non-Independent and Identically Distributed (non-IID) nature of data flows between the Metaverse and healthcare entities. The introduction of Digital Twin (DTs) has emerged as a groundbreaking innovation in the consumer health industry, offering real-time, dynamic digital representations of physical entities and providing invaluable insights and situational awareness. To address the multifaceted challenges, this paper introduces Decentralized SplitFed Learning (DSFL), a collaborative AI learning technique designed for deployment within the resource-constrained consumer health industry environment. DSFL optimizes resource utilization and mitigates computation burdens through decentralized model distribution. Furthermore, DSFL tackles the issue of non-IID data distribution within the dynamic Metaverse-driven consumer healthcare ecosystem by incorporating a clustering-based mechanism, which enhances learning performance by aligning the DTs’ representations of healthcare consumers’ real-time data statistics with the everchanging data streams within the Metaverse.

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