  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. Here, we propose an iterative approach that
is adversary-tolerant for least-squares problems. The algorithm utilizes simple
statistics to guarantee convergence and is capable of learning the adversarial
distributions. Additionally, the efficiency of the proposed method is shown in
simulations in the presence of adversaries. The results demonstrate the great
capability of such methods to tolerate different levels of adversary rates and
to identify the erroneous workers with high accuracy.
