IEEE Access (Jan 2024)

Blockchain-Empowered Metaverse: Decentralized Crowdsourcing and Marketplace for Trading Machine Learning Data and Models

  • Hung Duy Le,
  • Vu Tuan Truong,
  • Long Bao Le

DOI
https://doi.org/10.1109/ACCESS.2024.3401076
Journal volume & issue
Vol. 12
pp. 68556 – 68572

Abstract

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The Metaverse relies on advanced machine learning (ML) techniques to facilitate the seamless mapping between the virtual and physical realms. ML-based technologies also enable metaverse service providers (MSPs) to offer a diverse range of intelligent virtual services to metaverse users (MUs). However, it can be challenging for MSPs to collect sufficient metaverse data to train ML models by themselves. As a result, MSPs can be interested in seeking contributions from MUs in both ML data and models. To address these challenges, we propose MetaAICM, a blockchain-based framework that empowers the metaverse through two key components. Firstly, it incorporates a distributed crowdsourcing system that allows MSPs to gather metaverse data and ML models from MUs. Secondly, it features a decentralized marketplace, enabling MUs to proactively collect data and train ML models for sale using their metaverse devices and computing resources. MetaAICM leverages blockchain and smart contracts to achieve decentralization, ensuring security and privacy without relying on a trusted third-party authority or additional trust assumptions between MUs and MSPs. Numerical studies show that MetaAICM offers high processing performance and cost efficiency, while the framework is implemented on top of a consortium blockchain to show its feasibility.

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