  Recent studies have revealed that federated learning (FL), once considered
secure due to clients not sharing their private data with the server, is
vulnerable to attacks such as client-side training data distribution inference,
where a malicious client can recreate the victim's data. While various
countermeasures exist, they are not practical, often assuming server access to
some training data or knowledge of label distribution before the attack.
  In this work, we bridge the gap by proposing InferGuard, a novel
Byzantine-robust aggregation rule aimed at defending against client-side
training data distribution inference attacks. In our proposed InferGuard, the
server first calculates the coordinate-wise median of all the model updates it
receives. A client's model update is considered malicious if it significantly
deviates from the computed median update. We conduct a thorough evaluation of
our proposed InferGuard on five benchmark datasets and perform a comparison
with ten baseline methods. The results of our experiments indicate that our
defense mechanism is highly effective in protecting against client-side
training data distribution inference attacks, even against strong adaptive
attacks. Furthermore, our method substantially outperforms the baseline methods
in various practical FL scenarios.
