  The decentralized and privacy-preserving nature of federated learning (FL)
makes it vulnerable to backdoor attacks aiming to manipulate the behavior of
the resulting model on specific adversary-chosen inputs. However, most existing
defenses based on statistical differences take effect only against specific
attacks, especially when the malicious gradients are similar to benign ones or
the data are highly non-independent and identically distributed (non-IID). In
this paper, we revisit the distance-based defense methods and discover that i)
Euclidean distance becomes meaningless in high dimensions and ii) malicious
gradients with diverse characteristics cannot be identified by a single metric.
To this end, we present a simple yet effective defense strategy with
multi-metrics and dynamic weighting to identify backdoors adaptively.
Furthermore, our novel defense has no reliance on predefined assumptions over
attack settings or data distributions and little impact on benign performance.
To evaluate the effectiveness of our approach, we conduct comprehensive
experiments on different datasets under various attack settings, where our
method achieves the best defensive performance. For instance, we achieve the
lowest backdoor accuracy of 3.06% under the difficult Edge-case PGD, showing
significant superiority over previous defenses. The results also demonstrate
that our method can be well-adapted to a wide range of non-IID degrees without
sacrificing the benign performance.
