FedTADBench: Federated Time-Series Anomaly Detection Benchmark

TL;DR

This paper introduces FedTADBench, a comprehensive benchmark evaluating the performance of various time series anomaly detection algorithms within federated learning frameworks, addressing privacy concerns and decentralized data challenges.

Contribution

It provides the first extensive benchmark comparing anomaly detection algorithms and federated learning methods for time series data in decentralized settings.

Findings

Federated learning impacts anomaly detection performance.

Some federated methods outperform others in specific scenarios.

The benchmark offers insights into data partition effects on detection accuracy.

Abstract

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.