  With the proliferation of mobile sensing techniques, huge amounts of time
series data are generated and accumulated in various domains, fueling plenty of
real-world applications. In this setting, time series anomaly detection is
practically important. It endeavors to identify deviant samples from the normal
sample distribution in time series. Existing approaches generally assume that
all the time series is available at a central location. However, we are
witnessing the decentralized collection of time series due to the deployment of
various edge devices. To bridge the gap between the decentralized time series
data and the centralized anomaly detection algorithms, we propose a
Parameter-efficient Federated Anomaly Detection framework named PeFAD with the
increasing privacy concerns. PeFAD for the first time employs the pre-trained
language model (PLM) as the body of the client's local model, which can benefit
from its cross-modality knowledge transfer capability. To reduce the
communication overhead and local model adaptation cost, we propose a
parameter-efficient federated training module such that clients only need to
fine-tune small-scale parameters and transmit them to the server for update.
PeFAD utilizes a novel anomaly-driven mask selection strategy to mitigate the
impact of neglected anomalies during training. A knowledge distillation
operation on a synthetic privacy-preserving dataset that is shared by all the
clients is also proposed to address the data heterogeneity issue across
clients. We conduct extensive evaluations on four real datasets, where PeFAD
outperforms existing state-of-the-art baselines by up to 28.74%.
