Enhanced Federated Anomaly Detection Through Autoencoders Using Summary Statistics-Based Thresholding
This work addresses the problem of improving accuracy and robustness in federated anomaly detection for decentralized data systems, representing an incremental advancement in thresholding methods.
The study tackled the challenge of anomaly detection in federated learning by introducing a novel threshold calculation method using summary statistics, which consistently outperformed existing techniques across multiple datasets, particularly in non-IID scenarios.
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.