# An acceleration of decentralized SGD under general assumptions with low stochastic noise

arXiv: https://arxiv.org/abs/2011.07585 · HTML: https://mlsanity.com/paper/2011.07585/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration](https://mlsanity.com/paper/2410.07272/) (2024)
- [On Acceleration of Gradient-Based Empirical Risk Minimization using Local Polynomial Regression](https://mlsanity.com/paper/2204.07702/) (2022)
- [OledFL: Unleashing the Potential of Decentralized Federated Learning via Opposite Lookahead Enhancement](https://mlsanity.com/paper/2410.06482/) (2024)

## Abstract (arXiv)

Distributed optimization methods are actively researched by optimization community. Due to applications in distributed machine learning, modern research directions include stochastic objectives, reducing communication frequency, and time-varying communication network topology. Recently, an analysis unifying several centralized and decentralized approaches to stochastic distributed optimization was developed in Koloskova et al. (2020). In this work, we employ a Catalyst framework and accelerate the rates of Koloskova et al. (2020) in the case of low stochastic noise.