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

arXiv: https://arxiv.org/abs/2308.14783 · HTML: https://mlsanity.com/paper/2308.14783/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 26 references that are in the map.
- First posted on arXiv 28 Aug 2023.

## Builds on (most cited first, 12 of 12)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Federated Optimization:Distributed Optimization Beyond the Datacenter](https://mlsanity.com/paper/1511.03575/) (2015)
- [Asynchronous Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1710.06952/) (2017)
- [Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiency](https://mlsanity.com/paper/2102.13152/) (2021)
- [Communication-Efficient Distributed Dual Coordinate Ascent](https://mlsanity.com/paper/1409.1458/) (2014)
- [An Asynchronous Parallel Stochastic Coordinate Descent Algorithm](https://mlsanity.com/paper/1311.1873/) (2013)
- [Asynchronous Coordinate Descent under More Realistic Assumptions](https://mlsanity.com/paper/1705.08494/) (2017)
- [Anytime Stochastic Gradient Descent: A Time to Hear from all the Workers](https://mlsanity.com/paper/1810.02976/) (2018)
- [PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent](https://mlsanity.com/paper/1504.01365/) (2015)
- [Avoiding communication in primal and dual block coordinate descent methods](https://mlsanity.com/paper/1612.04003/) (2016)
- [A DAG Model of Synchronous Stochastic Gradient Descent in Distributed Deep Learning](https://mlsanity.com/paper/1805.03812/) (2018)
- [Asynchronous Distributed Semi-Stochastic Gradient Optimization](https://mlsanity.com/paper/1508.01633/) (2015)

## Abstract (arXiv)

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.