  While being an effective framework of learning a shared model across multiple
edge devices, federated learning (FL) is generally vulnerable to Byzantine
attacks from adversarial edge devices. While existing works on FL mitigate such
compromised devices by only aggregating a subset of the local models at the
server side, they still cannot successfully ignore the outliers due to
imprecise scoring rule. In this paper, we propose an effective Byzantine-robust
FL framework, namely dummy contrastive aggregation, by defining a novel scoring
function that sensitively discriminates whether the model has been poisoned or
not. Key idea is to extract essential information from every local models along
with the previous global model to define a distance measure in a manner similar
to triplet loss. Numerical results validate the advantage of the proposed
approach by showing improved performance as compared to the state-of-the-art
Byzantine-resilient aggregation methods, e.g., Krum, Trimmed-mean, and Fang.
