FedTADBench: Federated Time-Series Anomaly Detection Benchmark
This work addresses the need for standardized evaluation in federated time-series anomaly detection, which is crucial for applications like IoT and healthcare, but it is incremental as it benchmarks existing methods rather than proposing new ones.
The paper tackles the problem of evaluating time-series anomaly detection algorithms under federated learning settings, where data is decentralized and privacy-protected, by introducing FedTADBench, a benchmark that tests five detection algorithms and four federated methods across various experimental setups, providing extensive results and analysis.
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.