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..
FetchSGD: Communication-Efficient Federated Learning with Sketching
Authors: Daniel Rothchild, Ashwinee Panda, Enayat Ullah, Nikita Ivkin, Ion Stoica, Vladimir Braverman, Joseph Gonzalez, Raman Arora
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We prove that Fetch SGD has favorable convergence guarantees, and we demonstrate its empirical effectiveness by training two residual networks and a transformer model. |
| Researcher Affiliation | Collaboration | 1University of California, Berkeley, California, USA 2Johns Hopkins University, Baltimore, Maryland 3Amazon. |
| Pseudocode | Yes | Fetch SGD is presented in full in Algorithm 1. |
| Open Source Code | Yes | Code available at https://github.com/ kiddyboots216/Comm Efficient. Git commit at the time of camera-ready: 833ca44. |
| Open Datasets | Yes | CIFAR10 and CIFAR100 (Krizhevsky et al., 2009)... Federated EMNIST... (Caldas et al., 2018)... We finetune a pretrained GPT2 on the Persona Chat dataset... (Zhang et al., 2018). |
| Dataset Splits | Yes | CIFAR10 (CIFAR100) we use 10,000 (50,000) clients... We report accuracy on the test datasets... We report final accuracies on the validation dataset. |
| Hardware Specification | No | The paper does not specify the exact hardware (e.g., CPU, GPU models, cloud instance types) used for running the experiments. |
| Software Dependencies | No | We implement and compare Fetch SGD, gradient sparsification (local top-k), and Fed Avg using Py Torch (Paszke et al., 2019). |
| Experiment Setup | Yes | Details on which hyperparameters were chosen for each task can be found in Appendix A.1. |