# Distributed Gradient Descent with Coded Partial Gradient Computations

arXiv: https://arxiv.org/abs/1811.09271 · HTML: https://mlsanity.com/paper/1811.09271/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2018.

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

- [Gradient Coding with Dynamic Clustering for Straggler-Tolerant Distributed Learning](https://mlsanity.com/paper/2103.01206/) (2021)
- [Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent](https://mlsanity.com/paper/2201.12990/) (2022)
- [Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Bandits](https://mlsanity.com/paper/2202.08302/) (2022)
- [Pipelined Gradient Coding](https://mlsanity.com/paper/2607.20739/) (2026)

## Abstract (arXiv)

Coded computation techniques provide robustness against straggling servers in distributed computing, with the following limitations: First, they increase decoding complexity. Second, they ignore computations carried out by straggling servers; and they are typically designed to recover the full gradient, and thus, cannot provide a balance between the accuracy of the gradient and per-iteration completion time. Here we introduce a hybrid approach, called coded partial gradient computation (CPGC), that benefits from the advantages of both coded and uncoded computation schemes, and reduces both the computation time and decoding complexity.