  In the evolving landscape of Federated Learning (FL), a new type of attacks
concerns the research community, namely Data Poisoning Attacks, which threaten
the model integrity by maliciously altering training data. This paper
introduces a novel defensive framework focused on the strategic elimination of
adversarial users within a federated model. We detect those anomalies in the
aggregation phase of the Federated Algorithm, by integrating metadata gathered
by the local training instances with Differential Privacy techniques, to ensure
that no data leakage is possible. To our knowledge, this is the first proposal
in the field of FL that leverages metadata other than the model's gradients in
order to ensure honesty in the reported local models. Our extensive experiments
demonstrate the efficacy of our methods, significantly mitigating the risk of
data poisoning while maintaining user privacy and model performance. Our
findings suggest that this new user elimination approach serves us with a great
balance between privacy and utility, thus contributing to the arsenal of
arguments in favor of the safe adoption of FL in safe domains, both in academic
setting and in the industry.
