用于工业设备异常检测的个性化和差异化私有联合学习

IF 2.3 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE journal of radio frequency identification Pub Date : 2024-04-17 DOI:10.1109/JRFID.2024.3390142
Zhen Zhang;Weishan Zhang;Zhicheng Bao;Yifan Miao;Yuru Liu;Yikang Zhao;Rui Zhang;Wenyin Zhu
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引用次数: 0

摘要

联盟学习是一种分布式机器学习方法,可在保护数据隐私的同时实现协同训练。然而,在分布式场景中,工业设备的运行数据是动态和非独立同分布的(non-IID)。这种情况导致联合学习算法在工业异常检测任务中表现不佳。个性化联合学习是解决非独立同分布数据问题的可行方案,但在应对动态环境变化方面效果不佳。对模型实施定向更新,从而有效保持其稳定性,是应对动态挑战的解决方案之一。此外,尽管联合学习具有保护数据隐私的能力,但它仍然存在因差异隐私攻击而导致隐私泄露的风险。在本文中,我们提出了一种基于超网络和可信定向更新模型的个性化联合学习,为动态环境中拥有非 IID 数据的客户生成稳定的个性化模型。此外,我们还提出了一种参数可变的差分隐私机制,以减轻受损的差分攻击。我们使用来自三个不同工厂的真实空调数据集评估了所提方法执行异常检测任务的能力。结果表明,我们的框架优于现有的个性化联合学习方法,平均准确率提高了 11.32%。此外,实验结果表明,该框架可以抵御差异攻击,同时保持较高的准确率。
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A Personalized and Differentially Private Federated Learning for Anomaly Detection of Industrial Equipment
Federated learning is a distributed machine learning approach that achieves collaborative training while protecting data privacy. However, in distributed scenarios, the operational data of industrial equipment is dynamic and non-independently identically distributed (non-IID). This situation leads to poor performance of federated learning algorithms in industrial anomaly detection tasks. Personalized federated learning is a viable solution to the non-IID data problem, but it is not effective in responding to dynamic environmental changes. Implementing directed updates to the model, thereby effectively maintaining its stability, is one of the solutions for addressing dynamic challenges. In addition, even though federated learning has the ability to protect data privacy, it still has the risk of privacy leakage due to differential privacy attacks. In this paper, we propose a personalized federated learning based on hypernetwork and credible directed update of models to generate stable personalized models for clients with non-IID data in a dynamic environment. Furthermore, we propose a parameter-varying differential privacy mechanism to mitigate compromised differential attacks. We evaluate the capability of the proposed method to perform the anomaly detection task using real air conditioning datasets from three distinct factories. The results demonstrate that our framework outperforms existing personalized federated learning methods with an average accuracy improvement of 11.32%. Additionally, experimental results demonstrate that the framework can withstand differential attacks while maintaining high accuracy.
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