# S-NEAR-DGD: A Flexible Distributed Stochastic Gradient Method for Inexact Communication

arXiv: https://arxiv.org/abs/2102.00121 · HTML: https://mlsanity.com/paper/2102.00121/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2021.

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

- [DESTRESS: Computation-Optimal and Communication-Efficient Decentralized Nonconvex Finite-Sum Optimization](https://mlsanity.com/paper/2110.01165/) (2021)
- [On the Computation-Communication Trade-Off with A Flexible Gradient Tracking Approach](https://mlsanity.com/paper/2306.07159/) (2023)
- [Communication-Efficient Stochastic Distributed Learning](https://mlsanity.com/paper/2501.13516/) (2025)
- [Pareto-optimal Trade-offs Between Communication and Computation with Flexible Gradient Tracking](https://mlsanity.com/paper/2509.18129/) (2025)
- [Decentralized Stochastic Subgradient-type Methods with Communication Compression for Nonsmooth Nonconvex Optimization](https://mlsanity.com/paper/2607.01755/) (2026)

## Abstract (arXiv)

We present and analyze a stochastic distributed method (S-NEAR-DGD) that can tolerate inexact computation and inaccurate information exchange to alleviate the problems of costly gradient evaluations and bandwidth-limited communication in large-scale systems. Our method is based on a class of flexible, distributed first order algorithms that allow for the trade-off of computation and communication to best accommodate the application setting. We assume that all the information exchange between nodes is subject to random distortion and that only stochastic approximations of the true gradients are available. Our theoretical results prove that the proposed algorithm converges linearly in expectation to a neighborhood of the optimal solution for strongly convex objective functions with Lipschitz gradients. We characterize the dependence of this neighborhood on algorithm and network parameters, the quality of the communication channel and the precision of the stochastic gradient approximations used. Finally, we provide numerical results to evaluate the empirical performance of our method.