Distributed Dual Coordinate Ascent with Imbalanced Data on a General Tree Network

TL;DR

This paper studies how imbalanced data affects distributed dual coordinate ascent in tree networks and proposes a delayed generalized method to improve convergence speed, validated by numerical experiments.

Contribution

It introduces a novel delayed generalized distributed dual coordinate ascent algorithm that accounts for data imbalance, enhancing convergence in distributed machine learning.

Findings

Improved convergence speed with the proposed method.

Effective handling of imbalanced data in distributed settings.

Numerical experiments confirm the method's efficiency.

Abstract

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.