Distributed Dual Coordinate Ascent with Imbalanced Data on a General
  Tree Network
Myung Cho Lifeng Lai Weiyu Xu
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.