Work

Enhanced federated anomaly detection through autoencoders using summary statistics-based thresholding

Year: 2024

Type: article

Abstract: In Federated Learning, Anomaly Detection poses significant challenges due to the decentralized nature of data, especially under Non-IID distributions. This study proposes a federated threshold calculation method that aggregates summary statistics from normal and anomalous data across clients to create a global threshold for Anomaly Detection with federated Autoencoders, enhancing detection accuracy and robustness while ensuring privacy. Extensive experiments on datasets, including Credit Card Fraud Detection, Shuttle, and Covertype, show that our approach consistently outperforms existing federated and local threshold calculation methods. These findings highlight the potential of summary statistics in improving federated Anomaly Detection under Non-IID conditions.

Source: Scientific Reports

DOI: 10.1038/s41598-024-76961-2

Authors: Sofiane Laridi, Gregory Palmer, Kam-Ming Mark Tam

Institutions: Leibniz University Hannover, L3S Research Center, University of Hong Kong

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,590

Funder: Gottfried Wilhelm Leibniz Universität Hannover