The emerging industrial metaverses realize the mapping and expanding operations of physical industry into virtual space for significantly upgrading intelligent manufacturing. The industrial metaverses obtain data from various production and operation lines by Industrial Internet of Things (IIoT), and thus conduct effective data analysis and decision-making, thereby enhancing the production efficiency of the physical space, reducing operating costs, and maximizing commercial value. However, there still exist bottlenecks when integrating metaverses into IIoT, such as the privacy leakage of sensitive data with commercial secrets, IIoT sensing data freshness, and incentives for sharing these data. In this paper, we design a user-defined privacy-preserving framework with decentralized federated learning for the industrial metaverses. To further improve privacy protection of industrial metaverse, a cross-chain empowered federated learning framework is further utilized to perform decentralized, secure, and privacy-preserving data training on both physical and virtual spaces through a hierarchical blockchain architecture with a main chain and multiple subchains. Moreover, we introduce the age of information as the data freshness metric and thus design an age-based contract model to motivate data sensing among IIoT nodes. Numerical results indicate the efficiency of the proposed framework and incentive mechanism in the industrial metaverses.

翻译：新兴的工业元体实现了物理工业的测绘和扩展,将实际工业的运行发展成虚拟空间,从而大大提升智能制造业。工业元体通过物的工业互联网(IIoT)从各种生产和运营线获取数据,从而进行有效的数据分析和决策,从而提高物理空间的生产效率,降低运营成本,并最大限度地发挥商业价值。然而,在将基因变化纳入IIoT时,仍然存在瓶颈,例如敏感数据与商业秘密的隐私泄漏、IIoT感测数据新鲜度以及分享这些数据的奖励措施。在本文件中,我们设计了一个用户定义的隐私保护框架,将工业元体的联邦学习分散进行。为了进一步改善对工业元体的隐私保护,进一步利用一个跨链赋权的联结学习框架,通过具有主链和多个次链的分级链链链,对物理空间和虚拟空间进行分散、安全和保密的数据培训。此外,我们介绍信息时代作为数据新鲜度衡量标准,从而设计一个基于年龄的合同模型,以激励IIT的元元元模型和拟议的工业激励机制的效率。