Liu Shun, Zhou Funa, Tang Shanjie, Hu Xiong, Wang Chaoge, Wang Tianzhen
School of Logistic Engineering, Shanghai Maritime University, Shanghai 201306, China.
Entropy (Basel). 2023 Oct 21;25(10):1470. doi: 10.3390/e25101470.
In cases where a client suffers from completely unlabeled data, unsupervised learning has difficulty achieving an accurate fault diagnosis. Semi-supervised federated learning with the ability for interaction between a labeled client and an unlabeled client has been developed to overcome this difficulty. However, the existing semi-supervised federated learning methods may lead to a negative transfer problem since they fail to filter out unreliable model information from the unlabeled client. Therefore, in this study, a dynamic semi-supervised federated learning fault diagnosis method with an attention mechanism (SSFL-ATT) is proposed to prevent the federation model from experiencing negative transfer. A federation strategy driven by an attention mechanism was designed to filter out the unreliable information hidden in the local model. SSFL-ATT can ensure the federation model's performance as well as render the unlabeled client capable of fault classification. In cases where there is an unlabeled client, compared to the existing semi-supervised federated learning methods, SSFL-ATT can achieve increments of 9.06% and 12.53% in fault diagnosis accuracy when datasets provided by Case Western Reserve University and Shanghai Maritime University, respectively, are used for verification.
在客户拥有完全无标签数据的情况下，无监督学习难以实现准确的故障诊断。为克服这一困难，已开发出具有标记客户与无标记客户之间交互能力的半监督联邦学习。然而，现有的半监督联邦学习方法可能会导致负迁移问题，因为它们无法从无标记客户中过滤出不可靠的模型信息。因此，在本研究中，提出了一种带有注意力机制的动态半监督联邦学习故障诊断方法（SSFL-ATT），以防止联邦模型出现负迁移。设计了一种由注意力机制驱动的联邦策略，以过滤隐藏在局部模型中的不可靠信息。SSFL-ATT可以确保联邦模型的性能，同时使无标记客户能够进行故障分类。在存在无标记客户的情况下，与现有的半监督联邦学习方法相比，当分别使用凯斯西储大学和上海海事大学提供的数据集进行验证时，SSFL-ATT在故障诊断准确率上可分别提高9.06%和12.53%。
