The Industrial Internet of Things (IIoT) plays a pivotal role in steering enterprises towards comprehensive digital transformation and fostering intelligent production, which serves as a critical pillar of Industry 4.0. Digital twin (DT) emerges as a highly promising technology, enabling the digital transformation of the IIoT by seamlessly bridging physical systems with digital spaces. However, the overall service quality of the IIoT is severely impacted by the resource-limited devices and the massive, heterogeneous and sensitive data in the IIoT. As an innovative distributed machine learning paradigm, federated learning (FL) inherently possesses advantages in handling private and heterogeneous data. In this paper, we propose a novel framework integrating L with T-nabled IoT, termed FDEI, which combines the merits of both to improve service quality while maintaining trustworthiness. To enhance the modeling efficiency, we develop FedOA, an daptive ptimization L method that dynamically adjusts the local update coefficient and model compression rate in resource-limited IIoT scenarios, to construct the FDEI model. Specifically, leveraging the interdependence between the two variables, we conduct a theoretical analysis of the model convergence rate and derive the associated convergence bounds. Building upon the theoretical analysis, we further propose a joint adaptive adjustment strategy by optimizing the two variables across various clients to minimize runtime differences and accelerate the convergence rate. Numerical results demonstrate that our proposed approach achieves an approximate 68% improvement in convergence speed and a reduction of approximately 66% in traffic consumption compared to the benchmarks (e.g., FedAvg, AFL, and CSFL).

中文翻译：

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自适应优化联合学习在工业物联网中实现数字孪生


工业物联网（IIoT）在引导企业全面数字化转型、培育智能生产方面发挥着关键作用，是工业4.0的重要支柱。数字孪生 (DT) 作为一项极具前景的技术而出现，通过将物理系统与数字空间无缝连接来实现工业物联网的数字化转型。然而，工业物联网的设备资源有限，数据海量、异构、敏感，严重影响了工业物联网的整体服务质量。作为一种创新的分布式机器学习范式，联邦学习（FL）在处理私有和异构数据方面具有天然的优势。在本文中，我们提出了一种将 L 与支持 T 的物联网相结合的新颖框架，称为 FDEI，它结合了两者的优点，在保持可信度的同时提高服务质量。为了提高建模效率，我们开发了FedOA，一种自适应优化L方法，可以在资源有限的IIoT场景中动态调整本地更新系数和模型压缩率，以构建FDEI模型。具体来说，利用两个变量之间的相互依赖性，我们对模型收敛率进行理论分析并推导出相关的收敛界限。基于理论分析，我们进一步提出了一种联合自适应调整策略，通过优化不同客户端的两个变量来最小化运行时间差异并加快收敛速度​​。数值结果表明，与基准（例如 FedAvg、AFL 和 CSFL）相比，我们提出的方法使收敛速度提高了约 68%，流量消耗减少了约 66%。