论文标题
为对抗工人分布式随机Kaczmarz
Distributed randomized Kaczmarz for the adversarial workers
论文作者
论文摘要
开发对对抗性或损坏的工人存在强大的大规模分布式方法是使实际问题实用的这种方法的重要组成部分。在这里，我们提出了一种迭代方法，该方法对最小二乘问题具有对手耐受性。该算法利用简单的统计数据来保证收敛，并能够学习对抗分布。此外，在存在对手的情况下，在模拟中显示了所提出方法的效率。结果表明，这种方法具有耐受不同水平的对手率并以高精度确定错误的工人的能力。
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.
