In this paper, we investigate the impact of imbalanced data on the convergence of distributed dual coordinate ascent in a tree network for solving an empirical loss minimization problem in distributed machine learning. To address this issue, we propose a method called delayed generalized distributed dual coordinate ascent that takes into account the information of the imbalanced data, and provide the analysis of the proposed algorithm. Numerical experiments confirm the effectiveness of our proposed method in improving the convergence speed of distributed dual coordinate ascent in a tree network.

翻译：本文研究了数据不平衡对树形网络分布式对偶坐标上升法收敛性的影响，该方法是分布式机器学习中求解经验损失最小化问题的一种方法。为解决此问题，我们提出一种名为延迟广义分布式对偶坐标上升法的方法，该方法考虑了不平衡数据的信息，并给出了所提算法的分析。数值实验验证了该方法在提高树形网络分布式对偶坐标上升法收敛速度方面的有效性。