  The collaborative nature of federated learning (FL) poses a major threat in
the form of manipulation of local training data and local updates, known as the
Byzantine poisoning attack. To address this issue, many Byzantine-robust
aggregation rules (AGRs) have been proposed to filter out or moderate
suspicious local updates uploaded by Byzantine participants.
  This paper introduces a novel approach called AGRAMPLIFIER, aiming to
simultaneously improve the robustness, fidelity, and efficiency of the existing
AGRs. The core idea of AGRAMPLIFIER is to amplify the "morality" of local
updates by identifying the most repressive features of each gradient update,
which provides a clearer distinction between malicious and benign updates,
consequently improving the detection effect. To achieve this objective, two
approaches, namely AGRMP and AGRXAI, are proposed. AGRMP organizes local
updates into patches and extracts the largest value from each patch, while
AGRXAI leverages explainable AI methods to extract the gradient of the most
activated features. By equipping AGRAMPLIFIER with the existing
Byzantine-robust mechanisms, we successfully enhance the model's robustness,
maintaining its fidelity and improving overall efficiency.
  AGRAMPLIFIER is universally compatible with the existing Byzantine-robust
mechanisms. The paper demonstrates its effectiveness by integrating it with all
mainstream AGR mechanisms. Extensive evaluations conducted on seven datasets
from diverse domains against seven representative poisoning attacks
consistently show enhancements in robustness, fidelity, and efficiency, with
average gains of 40.08%, 39.18%, and 10.68%, respectively.
