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Communication-Efficient Federated Learning for Digital Twin Edge Networks in Industrial IoT

机译：工业IOT中数字双边缘网络的通信高效联合学习

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摘要

The rapid development of artificial intelligence and 5G paradigm, opens up new possibilities for emerging applications in industrial Internet of Things (IIoT). However, the large amount of data, the limited resources of Internet of Things devices, and the increasing concerns of data privacy, are major obstacles to improve the quality of services in IIoT. In this article, we propose the digital twin edge networks (DITENs) by incorporating digital twin into edge networks to fill the gap between physical systems and digital spaces. We further leverage the federated learning to construct digital twin models of IoT devices based on their running data. Moreover, to mitigate the communication overhead, we propose an asynchronous model update scheme and formulate the federated learning scheme as an optimization problem. We further decompose the problem and solve the subproblems based on the deep neural network model. Numerical results show that our proposed federated learning scheme for DITEN improves the communication efficiency and reduces the transmission energy cost.
机译：人工智能的快速发展和5G范式，为工业互联网（IIOT）开辟了新兴应用的新可能性。但是，大量数据，物联网资源有限，以及数据隐私的越来越多的问题，是提高IIOT服务质量的主要障碍。在本文中，我们通过将数字双胞胎结合到边缘网络来填充物理系统和数字空间之间的间隙来提出数字双边缘网络（DITEN）。我们进一步利用联合学习基于运行数据构建IOT设备的数字双胞胎模型。此外，为了减轻通信开销，我们提出了一种异步模型更新方案，并将联合学习方案制定为优化问题。我们进一步分解了问题并基于深神经网络模型解决子问题。数值结果表明，我们提出的DITEN的联合学习方案提高了通信效率并降低了传输能源成本。

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