Despite the fact that many anomaly detection approaches have been developed for multivariate time series data, limited effort has been made on federated settings in which multivariate time series data are heterogeneously distributed among different edge devices while data sharing is prohibited. In this paper, we investigate the problem of federated unsupervised anomaly detection and present a Federated Exemplar-based Deep Neural Network (Fed-ExDNN) to conduct anomaly detection for multivariate time series data on different edge devices. Specifically, we first design an Exemplar-based Deep Neural network (ExDNN) to learn local time series representations based on their compatibility with an exemplar module which consists of hidden parameters learned to capture varieties of normal patterns on each edge device. Next, a constrained clustering mechanism (FedCC) is employed on the centralized server to align and aggregate the parameters of different local exemplar modules to obtain a unified global exemplar module. Finally, the global exemplar module is deployed together with a shared feature encoder to each edge device and anomaly detection is conducted by examining the compatibility of testing data to the exemplar module. Fed-ExDNN captures local normal time series patterns with ExDNN and aggregates these patterns by FedCC, and thus can handle the heterogeneous data distributed over different edge devices simultaneously. Thoroughly empirical studies on six public datasets show that ExDNN and Fed-ExDNN can outperform state-of-the-art anomaly detection algorithms and federated learning techniques.

中文翻译：

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多变量时间序列数据的深度联合异常检测

尽管已经为多变量时间序列数据开发了许多异常检测方法，但在联邦设置中所做的努力有限，其中多变量时间序列数据异构分布在不同的边缘设备之间，同时禁止数据共享。在本文中，我们研究了联合无监督异常检测的问题，并提出了一种基于联合示例的深度神经网络 (Fed-ExDNN) 来对不同边缘设备上的多变量时间序列数据进行异常检测。具体来说，我们首先设计了一个基于示例的深度神经网络 (ExDNN)，以基于它们与示例模块的兼容性来学习本地时间序列表示，该示例模块由学习的隐藏参数组成，以捕获每个边缘设备上的各种正常模式。下一个，在集中式服务器上采用约束聚类机制（FedCC）对不同局部样本模块的参数进行对齐聚合，得到统一的全局样本模块。最后，全局示例模块与共享特征编码器一起部署到每个边缘设备，并通过检查测试数据与示例模块的兼容性来进行异常检测。Fed-ExDNN 使用 ExDNN 捕获局部正态时间序列模式，并通过 FedCC 聚合这些模式，从而可以同时处理分布在不同边缘设备上的异构数据。对六个公共数据集的彻底实证研究表明，ExDNN 和 Fed-ExDNN 的性能优于最先进的异常检测算法和联邦学习技术。