Distributed Dual Coordinate Ascent with Imbalanced Data on a General Tree Network
Machine Learning
2023-08-30 v1 Distributed, Parallel, and Cluster Computing
Information Theory
math.IT
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.
Cite
@article{arxiv.2308.14783,
title = {Distributed Dual Coordinate Ascent with Imbalanced Data on a General Tree Network},
author = {Myung Cho and Lifeng Lai and Weiyu Xu},
journal= {arXiv preprint arXiv:2308.14783},
year = {2023}
}
Comments
To be published in IEEE 2023 Workshop on Machine Learning for Signal Processing (MLSP)