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Failure-tolerant Distributed Learning for Anomaly Detection in Wireless Networks

arXiv 2303.13015 ↗First posted 23 Mar 2023cs.LG cs.AI cs.DCcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 39 references that are in the map. First posted on arXiv 23 Mar 2023.

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References · 19

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7 more
  1. 2021Flexible Clustered Federated Learning for Client-Level Data Distribution Shiftcited by 47
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