  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.
