# Communication-Censored Distributed Stochastic Gradient Descent

arXiv: https://arxiv.org/abs/1909.03631 · HTML: https://mlsanity.com/paper/1909.03631/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2019.

## Built on by (most cited first, 4 of 4)

- [Zeroth and First Order Stochastic Frank-Wolfe Algorithms for Constrained Optimization](https://mlsanity.com/paper/2107.06534/) (2021)
- [RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protection](https://mlsanity.com/paper/2402.19054/) (2024)
- [Distributed stochastic proximal algorithm with random reshuffling for non-smooth finite-sum optimization](https://mlsanity.com/paper/2111.03820/) (2021)
- [A Proportional-Integral Controller-Incorporated SGD Algorithm for High Efficient Latent Factor Analysis](https://mlsanity.com/paper/2508.17609/) (2025)

## Abstract (arXiv)

This paper develops a communication-efficient algorithm to solve the stochastic optimization problem defined over a distributed network, aiming at reducing the burdensome communication in applications such as distributed machine learning.Different from the existing works based on quantization and sparsification, we introduce a communication-censoring technique to reduce the transmissions of variables, which leads to our communication-Censored distributed Stochastic Gradient Descent (CSGD) algorithm. Specifically, in CSGD, the latest mini-batch stochastic gradient at a worker will be transmitted to the server if and only if it is sufficiently informative. When the latest gradient is not available, the stale one will be reused at the server. To implement this communication-censoring strategy, the batch-size is increasing in order to alleviate the effect of stochastic gradient noise. Theoretically, CSGD enjoys the same order of convergence rate as that of SGD, but effectively reduces communication. Numerical experiments demonstrate the sizable communication saving of CSGD.