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.

翻译：开发对存在对抗性或损坏工作节点具有鲁棒性的大规模分布式方法，是使此类方法在实际问题中具有实用性的重要环节。本文针对凸优化问题提出了一种具有抗攻击能力的迭代方法。该方法通过利用简单统计量确保收敛性，并能适应对抗性数据分布。此外，仿真实验表明，所提方法在存在对抗者的情况下能高效求解凸优化问题。通过仿真，我们验证了该方法在对抗环境下的有效性：既能高精度识别对抗性工作节点，又能容忍不同强度的对抗攻击率。