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

Myung Cho; Lifeng Lai; Weiyu Xu

doi:10.48550/arxiv.2308.14783

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

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

Myung Cho, Lifeng Lai and Weiyu Xu

ArXiv.org

Cornell University

08/28/2023

DOI: 10.48550/arxiv.2308.14783

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Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

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.

Details

Title: Subtitle: Distributed Dual Coordinate Ascent with Imbalanced Data on a General Tree Network
Creators: Myung Cho
Lifeng Lai
Weiyu Xu
Resource Type: Preprint
Publication Details: ArXiv.org
DOI: 10.48550/arxiv.2308.14783
ISSN: 2331-8422
Publisher: Cornell University
Language: English
Date posted: 08/28/2023
Academic Unit: Electrical and Computer Engineering
Record Identifier: 9984459656302771

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