# Compressed Distributed Gradient Descent: Communication-Efficient Consensus over Networks

arXiv: https://arxiv.org/abs/1812.04048 · HTML: https://mlsanity.com/paper/1812.04048/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2018.

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

- [Finite-Bit Quantization For Distributed Algorithms With Linear Convergence](https://mlsanity.com/paper/2107.11304/) (2021)
- [Quantization for decentralized learning under subspace constraints](https://mlsanity.com/paper/2209.07821/) (2022)
- [DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization](https://mlsanity.com/paper/2212.02376/) (2022)
- [Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining](https://mlsanity.com/paper/2308.03035/) (2023)

## Abstract (arXiv)

Network consensus optimization has received increasing attention in recent years and has found important applications in many scientific and engineering fields. To solve network consensus optimization problems, one of the most well-known approaches is the distributed gradient descent method (DGD). However, in networks with slow communication rates, DGD's performance is unsatisfactory for solving high-dimensional network consensus problems due to the communication bottleneck. This motivates us to design a communication-efficient DGD-type algorithm based on compressed information exchanges. Our contributions in this paper are three-fold: i) We develop a communication-efficient algorithm called amplified-differential compression DGD (ADC-DGD) and show that it converges under em any unbiased compression operator; ii) We rigorously prove the convergence performances of ADC-DGD and show that they match with those of DGD without compression; iii) We reveal an interesting phase transition phenomenon in the convergence speed of ADC-DGD. Collectively, our findings advance the state-of-the-art of network consensus optimization theory.