# Improving Distributed Gradient Descent Using Reed-Solomon Codes

arXiv: https://arxiv.org/abs/1706.05436 · HTML: https://mlsanity.com/paper/1706.05436/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2017.

## Built on by (most cited first, 14 of 14)

- [Gradient Coding with Dynamic Clustering for Straggler-Tolerant Distributed Learning](https://mlsanity.com/paper/2103.01206/) (2021)
- [Soft BIBD and Product Gradient Codes](https://mlsanity.com/paper/2105.05231/) (2021)
- [Adaptive Stochastic Gradient Descent for Fast and Communication-Efficient Distributed Learning](https://mlsanity.com/paper/2208.03134/) (2022)
- [Gradient Coding with Iterative Block Leverage Score Sampling](https://mlsanity.com/paper/2308.03096/) (2023)
- [Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent](https://mlsanity.com/paper/2201.12990/) (2022)
- [Optimization-based Block Coordinate Gradient Coding for Mitigating Partial Stragglers in Distributed Learning](https://mlsanity.com/paper/2206.02450/) (2022)
- [Sequential Gradient Coding For Straggler Mitigation](https://mlsanity.com/paper/2211.13802/) (2022)
- [Erasure Coded Neural Network Inference via Fisher Averaging](https://mlsanity.com/paper/2409.01420/) (2024)
- [Utilizing Redundancy in Cost Functions for Resilience in Distributed Optimization and Learning](https://mlsanity.com/paper/2110.10858/) (2021)
- [Impact of Redundancy on Resilience in Distributed Optimization and Learning](https://mlsanity.com/paper/2211.08622/) (2022)
- [Fast and Straggler-Tolerant Distributed SGD with Reduced Computation Load](https://mlsanity.com/paper/2304.08589/) (2023)
- [Iterative Sketching for Secure Coded Regression](https://mlsanity.com/paper/2308.04185/) (2023)
- [Design and Optimization of Hierarchical Gradient Coding for Distributed Learning at Edge Devices](https://mlsanity.com/paper/2406.10831/) (2024)
- [Pipelined Gradient Coding](https://mlsanity.com/paper/2607.20739/) (2026)

## Abstract (arXiv)

Today's massively-sized datasets have made it necessary to often perform computations on them in a distributed manner. In principle, a computational task is divided into subtasks which are distributed over a cluster operated by a taskmaster. One issue faced in practice is the delay incurred due to the presence of slow machines, known as stragglers. Several schemes, including those based on replication, have been proposed in the literature to mitigate the effects of stragglers and more recently, those inspired by coding theory have begun to gain traction. In this work, we consider a distributed gradient descent setting suitable for a wide class of machine learning problems. We adapt the framework of Tandon et al. (arXiv:1612.03301) and present a deterministic scheme that, for a prescribed per-machine computational effort, recovers the gradient from the least number of machines f theoretically permissible, via an O(f2) decoding algorithm. We also provide a theoretical delay model which can be used to minimize the expected waiting time per computation by optimally choosing the parameters of the scheme. Finally, we supplement our theoretical findings with numerical results that demonstrate the efficacy of the method and its advantages over competing schemes.