Enhanced Federated Anomaly Detection Through Autoencoders Using Summary Statistics-Based Thresholding
Year: 2024
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Laridi, Sofiane, Palmer, Gregory, Tam, Kam-Ming Mark
Institutions: Leibniz University Hannover, L3S Research Center
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4403564524