面向智能故障诊断的基于双重对比学习与软标签的半监督联邦学习
Semi-Supervised Federated Learning via Dual Contrastive Learning and Soft Labeling for Intelligent Fault Diagnosis
AI总结：
本文提出半监督联邦学习框架SSFL-DCSL，通过双重对比损失、软标签和原型聚合解决分布式客户端标注数据稀缺与数据分布差异问题，在仅10%标注数据下较现有方法提升1.15%至7.85%准确率。
摘要：
智能故障诊断（IFD）在保障工业机械安全运行和提高生产效率方面发挥着至关重要的作用。然而，传统的监督式深度学习方法需要大量的训练数据和标签，而这些数据和标签往往分布在不同的客户端中。此外，数据标注成本高昂，使得标签难以获取。与此同时，客户端之间数据分布的差异也可能阻碍模型的性能。为应对这些挑战，本文提出了一种半监督联邦学习框架SSFL-DCSL，该框架融合了双重对比损失和软标签，以在仅有少量标注样本的分布式客户端中解决数据和标签稀缺问题，同时保护用户隐私。它能够在客户端利用无标签数据进行表征学习，并通过原型促进客户端之间的联合学习，从而实现相互知识共享并防止局部模型发散。具体而言，首先设计了一种基于拉普拉斯分布的样本加权函数，以缓解半监督训练过程中伪标签低置信度所带来的偏差。其次，引入双重对比损失以缓解由不同数据分布引起的模型发散，该损失包括局部对比损失和全局对比损失。第三，在服务器上通过加权平均对局部原型进行聚合，并利用动量进行更新，以在客户端之间共享知识。为评估所提出的SSFL-DCSL框架，在两个公开数据集和一个从工厂电机采集的数据集上进行了实验。在最具挑战性的任务中，即仅有10%的数据被标注时，所提出的SSFL-DCSL相比最先进的方法可将准确率提高1.15%至7.85%。
英文摘要：
Intelligent fault diagnosis (IFD) plays a crucial role in ensuring the safe operation of industrial machinery and improving production efficiency. However, traditional supervised deep learning methods require a large amount of training data and labels, which are often located in different clients. Additionally, the cost of data labeling is high, making labels difficult to acquire. Meanwhile, differences in data distribution among clients may also hinder the model's performance. To tackle these challenges, this paper proposes a semi-supervised federated learning framework, SSFL-DCSL, which integrates dual contrastive loss and soft labeling to address data and label scarcity for distributed clients with few labeled samples while safeguarding user privacy. It enables representation learning using unlabeled data on the client side and facilitates joint learning among clients through prototypes, thereby achieving mutual knowledge sharing and preventing local model divergence. Specifically, first, a sample weighting function based on the Laplace distribution is designed to alleviate bias caused by low confidence in pseudo labels during the semi-supervised training process. Second, a dual contrastive loss is introduced to mitigate model divergence caused by different data distributions, comprising local contrastive loss and global contrastive loss. Third, local prototypes are aggregated on the server with weighted averaging and updated with momentum to share knowledge among clients. To evaluate the proposed SSFL-DCSL framework, experiments are conducted on two publicly available datasets and a dataset collected on motors from the factory. In the most challenging task, where only 10\% of the data are labeled, the proposed SSFL-DCSL can improve accuracy by 1.15% to 7.85% over state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
