联邦SW-TSAD:基于SWGAN的联邦时间序列异常检测
Xiuxian Zhang1,2,3, Hongwei Zhao1,2,3, Weishan Zhang1,2,3
1Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
FedSW-TSAD增强了使用索博列夫-瓦瑟斯坦GAN的联合时间序列异常检测,以实现稳定的训练和强大的异常识别. 这种保护隐私的方法可以改善分散的传感器网络中的F1分数和梯度隐私.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 分散的时间序列数据收集对模型培训和数据隐私提出了挑战.
- 现有的联合异常检测方法由于客户端异质性而遭受不稳定的训练和糟糕的泛化.
- 单路检测方法缺乏表达力,无法有效处理各种异常.
研究的目的:
- 提出FedSW-TSAD,一种新的联合时间序列异常检测方法.
- 为了提高训练稳定性和泛化在联合异常检测.
- 确保在分散的传感器网络中进行强大的异常检测和隐私保护.
主要方法:
- 使用索波列夫-瓦瑟斯坦GAN (SWGAN) 来稳定对手训练.
- 来自重建和预测模块的综合区分信号,以提高稳定性.
- 实施了差异性隐私机制,用于保护隐私,使用L2-规范受限制的噪音注入.
主要成果:
- FedSW-TSAD表现出卓越的性能,平均F1得分比现有方法提高了14.37%.
- 该方法显示了对现实世界传感器数据集中的各种异常的增强稳定性.
- 在差异隐私机制下,渐变隐私得到了显著的改善.
结论:
- 在联邦环境中,FedSW-TSAD为保护隐私的异常检测提供了实用和有效的解决方案.
- 拟议的方法解决了分散的时间序列分析中的关键挑战.
- FedSW-TSAD对工业物联网,远程诊断和预测性维护具有重大影响.
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