Conference proceeding
Distributed Dual Coordinate Ascent With Imbalanced Data on a General Tree Network
2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP), pp.1-6
09/17/2023
DOI: 10.1109/MLSP55844.2023.10285903
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 - California State University,Department of Electrical and Computer Engineering,Northridge,CA,USALifeng Lai - University of California, DavisWeiyu Xu - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP), pp.1-6
- Publisher
- IEEE
- DOI
- 10.1109/MLSP55844.2023.10285903
- eISSN
- 2161-0371
- Grant note
- National Science Foundation (10.13039/100000001)
- Language
- English
- Date published
- 09/17/2023
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984502955302771
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