【Author】
Zhang, Peiying; Hong, Yanrong; Kumar, Neeraj; Alazab, Mamoun; Alshehri, Mohammad Dahman; Jiang, Chunxiao
【Source】IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
【Abstract】Under the times of the Industrial Internet of Things, the traditional centralized machine learning management method cannot deal with such huge data streams, and the problem of data privacy has aroused widespread concern. In view of these difficulties, in this article, we use the advantages of edge computing and federated learning, combined with the outstanding characteristics of the blockchain, to propose a secure data transmission method. First, we separate the local model updating process from the mobile device independent process; second, we add an edge server so that most of the computation is carried out on the server, which improves the learning efficiency; and finally, we use a distributed architecture of the blockchain to protect data security and privacy. Extensive simulation experiments show that the accuracy of our model can reach 98%. In addition, BC-EdgeFLs interception rate of illegal information can reach 0.8, which has good defensive capabilities. Therefore, the security of data transmission can be strongly guaranteed.
【Keywords】Blockchain; data transmission method; edge computing (EC); federated learning (FL); industrial Internet of Things (IIoT)
【标题】BC-EdgeFL：工业物联网环境中基于区块链辅助强化联邦学习的防御性传输模型
【摘要】在工业物联网时代，传统的集中式机器学习管理方式无法处理如此庞大的数据流，数据隐私问题引起了广泛关注。针对这些难点，本文利用边缘计算和联邦学习的优势，结合区块链的突出特点，提出了一种安全的数据传输方法。首先，我们将本地模型更新过程与移动设备独立过程分开；其次，我们增加了一个边缘服务器，使得大部分计算都在服务器上进行，提高了学习效率；最后，我们使用区块链的分布式架构来保护数据安全和隐私。大量的仿真实验表明，我们的模型的准确率可以达到 98%。此外，BC-EdgeFLs对非法信息的拦截率可以达到0.8，具有很好的防御能力。因此，可以有力地保证数据传输的安全性。
【关键词】区块链；数据传输方式；边缘计算（EC）；联邦学习（FL）；工业物联网 (IIoT)
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