Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning
Authors: Samuel Horváth, Peter Richtarik
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform several numerical experiments which validate our theoretical findings. Finally, we provide an experimental evaluation on an array of classification tasks with CIFAR10 dataset corroborating our theoretical findings. |
| Researcher Affiliation | Academia | Samuel Horváth and Peter Richtárik KAUST Thuwal, Saudi Arabia EMAIL |
| Pseudocode | Yes | Algorithm 1 DCSGD; Algorithm 2 DCSGD with Error Feedback |
| Open Source Code | No | The paper does not provide an explicit statement about releasing the source code for the described methodology or a direct link to a code repository. |
| Open Datasets | Yes | We do an evaluation on CIFAR10 dataset. |
| Dataset Splits | Yes | A validation accuracy is computed based on 10 % randomly selected training data. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | Our experimental results are based on a Python implementation of all the methods running in Py Torch. (No version numbers provided for Python or PyTorch). |
| Experiment Setup | Yes | We used a local batch size of 32. For every experiment, we randomly distributed the training dataset among 8 workers; each worker computes its local gradient-based on its own dataset. We consider VGG11 (Simonyan & Zisserman, 2015) and Res Net18 (He et al., 2016) models and step-sizes 0.1, 0.05 and 0.01. |