  In federated learning systems, clients are autonomous in that their behaviors
are not fully governed by the server. Consequently, a client may intentionally
or unintentionally deviate from the prescribed course of federated model
training, resulting in abnormal behaviors, such as turning into a malicious
attacker or a malfunctioning client. Timely detecting those anomalous clients
is therefore critical to minimize their adverse impacts. In this work, we
propose to detect anomalous clients at the server side. In particular, we
generate low-dimensional surrogates of model weight vectors and use them to
perform anomaly detection. We evaluate our solution through experiments on
image classification model training over the FEMNIST dataset. Experimental
results show that the proposed detection-based approach significantly
outperforms the conventional defense-based methods.
