  Developing large-scale distributed methods that are robust to the presence of
adversarial or corrupted workers is an important part of making such methods
practical for real-world problems. In this paper, we propose an iterative
approach that is adversary-tolerant for convex optimization problems. By
leveraging simple statistics, our method ensures convergence and is capable of
adapting to adversarial distributions. Additionally, the efficiency of the
proposed methods for solving convex problems is shown in simulations with the
presence of adversaries. Through simulations, we demonstrate the efficiency of
our approach in the presence of adversaries and its ability to identify
adversarial workers with high accuracy and tolerate varying levels of adversary
rates.
