# An Enhanced Gradient-Tracking Bound for Distributed Online Stochastic Convex Optimization

arXiv: https://arxiv.org/abs/2301.02855 · HTML: https://mlsanity.com/paper/2301.02855/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2023.

## Built on by (most cited first, 5 of 5)

- [The Effectiveness of Local Updates for Decentralized Learning under Data Heterogeneity](https://mlsanity.com/paper/2403.15654/) (2024)
- [Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates](https://mlsanity.com/paper/2306.05100/) (2023)
- [Distributed Random Reshuffling Methods with Improved Convergence](https://mlsanity.com/paper/2306.12037/) (2023)
- [A Bias-Correction Decentralized Stochastic Gradient Algorithm with Momentum Acceleration](https://mlsanity.com/paper/2501.19082/) (2025)
- [Decentralized Optimization with Topology-Independent Communication](https://mlsanity.com/paper/2509.14488/) (2025)

## Abstract (arXiv)

Gradient-tracking (GT) based decentralized methods have emerged as an effective and viable alternative method to decentralized (stochastic) gradient descent (DSGD) when solving distributed online stochastic optimization problems. Initial studies of GT methods implied that GT methods have worse network dependent rate than DSGD, contradicting experimental results. This dilemma has recently been resolved, and tighter rates for GT methods have been established, which improves upon DSGD. In this work, we establish more enhanced rates for GT methods under the online stochastic convex settings. We present an alternative approach for analyzing GT methods for convex problems and over static graphs. When compared to previous analyses, this approach allows us to establish enhanced network dependent rates.