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Adaptive Federated Learning and Digital Twin for Industrial Internet of Things
IEEE Transactions on Industrial Informatics ( IF 12.3 ) Pub Date : 2020-10-29 , DOI: 10.1109/tii.2020.3034674
Wen Sun , Shiyu Lei , Lu Wang , Zhiqiang Liu , Yan Zhang

Industrial Internet of Things (IoT) enables distributed intelligent services varying with the dynamic and realtime industrial environment to achieve Industry 4.0 benefits. In this article, we consider a new architecture of digital twin (DT) empowered Industrial IoT, where DTs capture the characteristics of industrial devices to assist federated learning. Noticing that DTs may bring estimation deviations from the actual value of device state, a trusted-based aggregation is proposed in federated learning to alleviate the effects of such deviation. We adaptively adjust the aggregation frequency of federated learning based on Lyapunov dynamic deficit queue and deep reinforcement learning (DRL), to improve the learning performance under the resource constraints. To further adapt to the heterogeneity of industrial IoT, a clustering-based asynchronous federated learning framework is proposed. Numerical results show that the proposed framework is superior to the benchmark in terms of learning accuracy, convergence, and energy saving.

中文翻译:

工业物联网的自适应联合学习和数字孪生

工业物联网(IoT)支持随动态和实时工业环境而变化的分布式智能服务,以实现工业4.0优势。在本文中,我们考虑了一种支持数字孪生(DT)的工业物联网的新架构,其中DT捕获了工业设备的特征以协助联合学习。考虑到DT可能会使估计偏差与设备状态的实际值产生偏差,因此在联合学习中提出了一种基于信任的聚合,以减轻这种偏差的影响。我们基于Lyapunov动态赤字队列和深度强化学习(DRL)自适应地调整联合学习的聚合频率,以在资源受限的情况下提高学习性能。为了进一步适应工业物联网的异构性,提出了一种基于聚类的异步联合学习框架。数值结果表明,该框架在学习准确性,收敛性和节能方面均优于基准。
更新日期:2020-10-29
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