【Author】
Zhang, Peiying; Sun, Hao; Situ, Jingyi; Jiang, Chunxiao; Xie, Dongliang
【Abstract】With the development of artificial intelligence and Internet of Things (IoT), the era of industry 4.0 has come. According to the prediction of IBM, with the continuous popularization of 5G technology, the IoT technology will be more widely used in factories. In recent years, federated learning has become a hot topic for Industrial Internet of Things (IIoT) researchers. However, many devices in the IIoT currently have a problem of low computing power, so these devices cannot perform well facing the tasks of training and updating models in federated learning. In order to solve the above problems, we introduce edge computing into the IIot, so that the device can complete the federated learning operation. In order to ensure the security of data transmission, blockchain is introduced as the main algorithm of equipment authentication in the system. What's more, in order to increase the efficiency and versatility of training model in IIoT, we introduce transfer learning to improve the system performance. The experimental results show that our algorithm can achieve high security and high training accuracy.
【Keywords】Industrial Internet of Things; Blockchain; Collaborative work; Transfer learning; Training; Edge computing; Production facilities; Federated learning; blockchain; Industrial Internet of Things; transfer learning; Security of Internet of Things
【标题】基于区块链和边缘计算的低算力工业物联网设备联合迁移学习
【摘要】随着人工智能和物联网（IoT）的发展，工业4.0时代已经到来。据IBM预测，随着5G技术的不断普及，物联网技术将在工厂中得到更广泛的应用。近年来，联邦学习已成为工业物联网 (IIoT) 研究人员的热门话题。然而，目前工业物联网中的许多设备都存在计算能力低的问题，因此这些设备在面对联邦学习中的训练和更新模型的任务时表现不佳。为了解决上述问题，我们将边缘计算引入到IIot中，使设备能够完成联邦学习操作。为了保证数据传输的安全，系统中引入了区块链作为设备认证的主要算法。此外，为了提高 IIoT 中训练模型的效率和通用性，我们引入了迁移学习来提高系统性能。实验结果表明，我们的算法可以实现高安全性和高训练精度。
【关键词】工业物联网；区块链；协作工作；迁移学习；训练;边缘计算；生产设施；联邦学习；区块链；工业物联网；迁移学习；物联网安全
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