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：开发能够抵御敌意或恶意工作者存在的大规模分布式方法，是使其在实际问题中具备实用性的重要环节。本文提出了一种针对凸优化问题的迭代方法，该算法具有抗敌意特性。通过利用简单统计量，我们的方法能够确保收敛性，并适应敌意分布。此外，通过存在敌意者的模拟实验，我们展示了所提方法在求解凸问题时的效率。仿真结果验证了该算法在敌意环境下的高效性、高精度识别敌意工作者的能力，以及对不同敌意率水平的鲁棒性。