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Author

Long-Bing Cao

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#federated learning Open access Oct 2026

LLM‐Powered Data Synthesis on Blockchain for Fusion Isomerism Learning in Heterogeneous Federated Systems

Severe data starvation, architectural heterogeneity and Byzantine vulnerabilities fundamentally impede the deployment of robust multiclass classification models in decentralised edge environments. To address these intertwined challenges, we propose fusion isomerism learning (FusionIL) , a secure and domain‐agnost...

Zhi-Hao Hao, Long-Bing Cao, Han Yu et al. · 0 citations
Book Open access Aug 2026

The Decision Twin: Metaverse Patient Digital Twins as Executable Clinical Reality

This work introduces the Metaverse Patient Digital Twin as a decision-grade clinical artifact defined by one requirement: every displayed claim or simulated scenario must be traceable to a versioned patient state, explicit assumptions, and replayable interaction logs.

Filippo Cenacchi, Long-Bing Cao, Deborah Richards · 0 citations
Book Open access Aug 2026

The Decision Twin: Metaverse Patient Digital Twins as Executable Clinical Reality

Clinical AI is no longer bottlenecked only by model performance; it is bottlenecked by the accountable interaction loop through which clinicians and patients inspect evidence, test alternatives, and remain responsible for decisions under uncertainty. We argue that today's digital-twin systems, XR interfaces, and founda...

Filippo Cenacchi, Longbing Cao, Deborah Richards · 0 citations

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