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The analysis of distributed techniques is often focused upon theirefficiency, without considering their robustness (or lack thereof). Such aconsideration is particularly important when devices or central servers canfail, which can potentially cripple distributed systems. When such failuresarise in wireless communications networks, important services that theyuse/provide (like anomaly detection) can be left inoperable and can result in acascade of security problems. In this paper, we present a novel method toaddress these risks by combining both flat- and star-topologies, combining theperformance and reliability benefits of both. We refer to this method as"Tol-FL", due to its increased failure-tolerance as compared to the techniqueof Federated Learning. Our approach both limits device failure risks whileoutperforming prior methods by up to 8% in terms of anomaly detection AUROC ina range of realistic settings that consider client as well as server failure,all while reducing communication costs. This performance demonstrates thatTol-FL is a highly suitable method for distributed model training for anomalydetection, especially in the domain of wireless networks.

Failure-tolerant Distributed Learning for Anomaly Detection in Wireless Networks