التفاصيل البيبلوغرافية
| العنوان: |
Failure-tolerant Distributed Learning for Anomaly Detection in Wireless Networks |
| المؤلفون: |
Katzef, Marc, Cullen, Andrew C., Alpcan, Tansu, Leckie, Christopher, Kopacz, Justin |
| سنة النشر: |
2023 |
| المجموعة: |
Computer Science |
| مصطلحات موضوعية: |
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Distributed, Parallel, and Cluster Computing |
| الوصف: |
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. |
| نوع الوثيقة: |
Working Paper |
| URL الوصول: |
http://arxiv.org/abs/2303.13015 |
| رقم الانضمام: |
edsarx.2303.13015 |
| قاعدة البيانات: |
arXiv |