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..
RelaySum for Decentralized Deep Learning on Heterogeneous Data
Authors: Thijs Vogels, Lie He, Anastasiia Koloskova, Sai Praneeth Karimireddy, Tao Lin, Sebastian U. Stich, Martin Jaggi
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experimental analysis and practical properties; In extensive tests on image- and text classification, Relay SGD performs better than both kinds of baselines at equal communication budget. |
| Researcher Affiliation | Academia | Thijs Vogels EPFL Lie He EPFL Anastasia Koloskova EPFL Tao Lin EPFL Sai Praneeth Karimireddy EPFL Sebastian U. Stich EPFL Martin Jaggi EPFL |
| Pseudocode | Yes | Algorithm 1 Relay SGD |
| Open Source Code | Yes | Our code is available at http://github.com/epfml/relaysgd. |
| Open Datasets | Yes | Cifar-10 [17]; Image Net [5]; AG news data [49] |
| Dataset Splits | No | The paper discusses partitioning training data across workers for heterogeneity ('We partition training data strictly across 16 workers and distribute the classes using a Dirichlet process [47, 20]'), but does not explicitly provide specific train/validation/test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or other processor specifications used for running experiments. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer' and 'VGG-11 architecture' but does not provide specific version numbers for these or any other ancillary software dependencies (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | No | The paper states 'We use 16-workers on Cifar-10, following the experimental details outlined in Appendix B and hyper-parameter tuning procedure from Appendix C,' indicating setup details are in appendices, but does not provide concrete hyperparameter values or detailed training configurations within the main text provided. |