Enhanced Federated Anomaly Detection Through Autoencoders Using Summary Statistics-Based Thresholding

TL;DR

This paper presents a federated anomaly detection method using autoencoders that employs summary statistics to compute a global threshold, enhancing detection accuracy and robustness in non-IID data scenarios.

Contribution

It introduces a novel federated threshold calculation technique based on summary statistics, improving anomaly detection performance while preserving data privacy.

Findings

Outperforms existing threshold methods in federated settings

Effective in handling non-IID data distributions

Improves scalability and detection accuracy

Abstract

In Federated Learning (FL), anomaly detection (AD) is a challenging task due to the decentralized nature of data and the presence of non-IID data distributions. This study introduces a novel federated threshold calculation method that leverages summary statistics from both normal and anomalous data to improve the accuracy and robustness of anomaly detection using autoencoders (AE) in a federated setting. Our approach aggregates local summary statistics across clients to compute a global threshold that optimally separates anomalies from normal data while ensuring privacy preservation. We conducted extensive experiments using publicly available datasets, including Credit Card Fraud Detection, Shuttle, and Covertype, under various data distribution scenarios. The results demonstrate that our method consistently outperforms existing federated and local threshold calculation techniques, particularly in handling non-IID data distributions. This study also explores the impact of different data distribution scenarios and the number of clients on the performance of federated anomaly detection. Our findings highlight the potential of using summary statistics for threshold calculation in improving the scalability and accuracy of federated anomaly detection systems.