PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection

Train and Evaluate

You can reproduce the experiment results by running each script in ./scripts/:

bash ./scripts/SMD.sh
bash ./scripts/PSM.sh
bash ./scripts/SWAT.sh
bash ./scripts/MSL.sh

Citation

Please cite the following paper if you use this repository in your research.

@inproceedings{xu2024pefad,
  title={PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection},
  author={Xu, Ronghui and Miao, Hao and Wang, Senzhang and Yu, Philip S and Wang, Jianxin},
  booktitle={SIGKDD},
  year={2024}
}

Datasets

SMD, PSM, SWaT, and MSL can be downloaded from Google Drive.

Biref Dataset Details

• Server Machine Dataset (SMD) is a 5-week-long dataset collected from a large Internet company with a time granularity of 1 minute. Please refer to Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network.
• Pooled Server Metrics (PSM) is a public dateset collected internally from multiple application server nodes at eBay. Please refer to Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization.
• Secure Water Treatment (SWaT) is obtained from 51 sensors of the critical infrastructure system under continuous operations. Please refer to SWaT: a water treatment testbed for research and training on ICS security.
• NASA's Mars Science Laboratory (MSL) dataset, collected during the spacecraft's journey to Mars, is a valuable resource accessible through NASA-designated data centers. Please refer to Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding.

Environment

• Python Version: 3.8
• PyTorch Version: 1.7.1
• Run the following script for environment configuration.

  pip install -r requirements.txt