  Time series anomaly detection strives to uncover potential abnormal behaviors
and patterns from temporal data, and has fundamental significance in diverse
application scenarios. Constructing an effective detection model usually
requires adequate training data stored in a centralized manner, however, this
requirement sometimes could not be satisfied in realistic scenarios. As a
prevailing approach to address the above problem, federated learning has
demonstrated its power to cooperate with the distributed data available while
protecting the privacy of data providers. However, it is still unclear that how
existing time series anomaly detection algorithms perform with decentralized
data storage and privacy protection through federated learning. To study this,
we conduct a federated time series anomaly detection benchmark, named
FedTADBench, which involves five representative time series anomaly detection
algorithms and four popular federated learning methods. We would like to answer
the following questions: (1)How is the performance of time series anomaly
detection algorithms when meeting federated learning? (2) Which federated
learning method is the most appropriate one for time series anomaly detection?
(3) How do federated time series anomaly detection approaches perform on
different partitions of data in clients? Numbers of results as well as
corresponding analysis are provided from extensive experiments with various
settings. The source code of our benchmark is publicly available at
https://github.com/fanxingliu2020/FedTADBench.
