  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.
