# Communication-Efficient Network-Distributed Optimization with Differential-Coded Compressors

arXiv: https://arxiv.org/abs/1912.03208 · HTML: https://mlsanity.com/paper/1912.03208/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2019.

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

- [CFedAvg: Achieving Efficient Communication and Fast Convergence in Non-IID Federated Learning](https://mlsanity.com/paper/2106.07155/) (2021)
- [Energy-efficient Decentralized Learning via Graph Sparsification](https://mlsanity.com/paper/2401.03083/) (2024)
- [Time-varying Mixing Matrix Design for Energy-efficient Decentralized Federated Learning](https://mlsanity.com/paper/2512.24069/) (2025)

## Abstract (arXiv)

Network-distributed optimization has attracted significant attention in recent years due to its ever-increasing applications. However, the classic decentralized gradient descent (DGD) algorithm is communication-inefficient for large-scale and high-dimensional network-distributed optimization problems. To address this challenge, many compressed DGD-based algorithms have been proposed. However, most of the existing works have high complexity and assume compressors with bounded noise power. To overcome these limitations, in this paper, we propose a new differential-coded compressed DGD (DC-DGD) algorithm. The key features of DC-DGD include: i) DC-DGD works with general SNR-constrained compressors, relaxing the bounded noise power assumption; ii) The differential-coded design entails the same convergence rate as the original DGD algorithm; and iii) DC-DGD has the same low-complexity structure as the original DGD due to a em self-noise-reduction effect. Moreover, the above features inspire us to develop a hybrid compression scheme that offers a systematic mechanism to minimize the communication cost. Finally, we conduct extensive experiments to verify the efficacy of the proposed DC-DGD and hybrid compressor.