Distributed Dual Coordinate Ascent with Imbalanced Data on a General Tree Network
This work addresses a specific bottleneck in distributed machine learning for scenarios with imbalanced data, representing an incremental improvement.
The paper tackled the problem of imbalanced data slowing convergence in distributed dual coordinate ascent on tree networks, and proposed a delayed generalized method that improved convergence speed, as confirmed by numerical experiments.
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.