Federated Variational Learning for Anomaly Detection in Multivariate Time Series
This addresses privacy-sensitive anomaly detection for Cyber-Physical Systems, but is incremental as it combines existing federated learning and VAE methods.
The paper tackles anomaly detection in multivariate time series from networked sensors by proposing an unsupervised federated learning framework using a Variational Autoencoder with ConvGRU, which leaves training data distributed at the edge to preserve privacy and handle large data quantities. Experiments on three real-world datasets show advantages over state-of-the-art models in performance and detection latency.
Anomaly detection has been a challenging task given high-dimensional multivariate time series data generated by networked sensors and actuators in Cyber-Physical Systems (CPS). Besides the highly nonlinear, complex, and dynamic natures of such time series, the lack of labeled data impedes data exploitation in a supervised manner and thus prevents an accurate detection of abnormal phenomenons. On the other hand, the collected data at the edge of the network is often privacy sensitive and large in quantity, which may hinder the centralized training at the main server. To tackle these issues, we propose an unsupervised time series anomaly detection framework in a federated fashion to continuously monitor the behaviors of interconnected devices within a network and alerts for abnormal incidents so that countermeasures can be taken before undesired consequences occur. To be specific, we leave the training data distributed at the edge to learn a shared Variational Autoencoder (VAE) based on Convolutional Gated Recurrent Unit (ConvGRU) model, which jointly captures feature and temporal dependencies in the multivariate time series data for representation learning and downstream anomaly detection tasks. Experiments on three real-world networked sensor datasets illustrate the advantage of our approach over other state-of-the-art models. We also conduct extensive experiments to demonstrate the effectiveness of our detection framework under non-federated and federated settings in terms of overall performance and detection latency.