Failure-tolerant Distributed Learning for Anomaly Detection in Wireless Networks
Abstract
The analysis of distributed techniques is often focused upon their efficiency, without considering their robustness (or lack thereof). Such a consideration is particularly important when devices or central servers can fail, which can potentially cripple distributed systems. When such failures arise in wireless communications networks, important services that they use/provide (like anomaly detection) can be left inoperable and can result in a cascade of security problems. In this paper, we present a novel method to address these risks by combining both flat- and star-topologies, combining the performance and reliability benefits of both. We refer to this method as "Tol-FL", due to its increased failure-tolerance as compared to the technique of Federated Learning. Our approach both limits device failure risks while outperforming prior methods by up to 8% in terms of anomaly detection AUROC in a range of realistic settings that consider client as well as server failure, all while reducing communication costs. This performance demonstrates that Tol-FL is a highly suitable method for distributed model training for anomaly detection, especially in the domain of wireless networks.
Cite
@article{arxiv.2303.13015,
title = {Failure-tolerant Distributed Learning for Anomaly Detection in Wireless Networks},
author = {Marc Katzef and Andrew C. Cullen and Tansu Alpcan and Christopher Leckie and Justin Kopacz},
journal= {arXiv preprint arXiv:2303.13015},
year = {2023}
}