【Author】
Lu, Yunlong; Huang, Xiaohong; Zhang, Ke; Maharjan, Sabita; Zhang, Yan
【Source】IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
【Abstract】Emerging technologies, such as digital twins and 6th generation (6G) mobile networks, have accelerated the realization of edge intelligence in industrial Internet of Things (IIoT). The integration of digital twin and 6G bridges the physical system with digital space and enables robust instant wireless connectivity. With increasing concerns on data privacy, federated learning has been regarded as a promising solution for deploying distributed data processing and learning in wireless networks. However, unreliable communication channels, limited resources, and lack of trust among users hinder the effective application of federated learning in IIoT. In this article, we introduce the digital twin wireless networks (DTWN) by incorporating digital twins into wireless networks, to migrate real-time data processing and computation to the edge plane. Then, we propose a blockchain empowered federated learning framework running in the DTWN for collaborative computing, which improves the reliability and security of the system and enhances data privacy. Moreover, to balance the learning accuracy and time cost of the proposed scheme, we formulate an optimization problem for edge association by jointly considering digital twin association, training data batch size, and bandwidth allocation. We exploit multiagent reinforcement learning to find an optimal solution to the problem. Numerical results on real-world dataset show that the proposed scheme yields improved efficiency and reduced cost compared to benchmark learning methods.
【Keywords】Digital twin; Collaborative work; Blockchain; Servers; Data models; Wireless networks; Blockchain; communication efficiency; digital twin; federated learning; wireless networks
【标题】数字孪生赋能 6G 网络中边缘关联的低延迟联邦学习和区块链
【摘要】数字孪生和第 6 代 (6G) 移动网络等新兴技术加速了工业物联网 (IIoT) 中边缘智能的实现。数字孪生与 6G 的集成将物理系统与数字空间联系起来，并实现了强大的即时无线连接。随着对数据隐私的日益关注，联邦学习被认为是在无线网络中部署分布式数据处理和学习的有前途的解决方案。然而，不可靠的通信渠道、有限的资源以及用户之间缺乏信任阻碍了联邦学习在 IIoT 中的有效应用。在本文中，我们介绍了数字孪生无线网络（DTWN），通过将数字孪生融入无线网络，将实时数据处理和计算迁移到边缘平面。然后，我们提出了一种在DTWN中运行的区块链授权联邦学习框架，用于协同计算，提高了系统的可靠性和安全性，增强了数据隐私。此外，为了平衡所提出方案的学习精度和时间成本，我们通过联合考虑数字孪生关联、训练数据批量大小和带宽分配来制定边缘关联的优化问题。我们利用多智能体强化学习来找到问题的最佳解决方案。真实世界数据集的数值结果表明，与基准学习方法相比，所提出的方案提高了效率并降低了成本。
【关键词】数字孪生；协作工作；区块链；服务器；数据模型；无线网络;区块链；沟通效率；数字孪生；联邦学习；无线网络
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