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..
Robust Collaborative Learning with Linear Gradient Overhead
Authors: Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Lê-Nguyên Hoang, Rafael Pinot, John Stephan
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our theory by experiments on image classification and make our code available at https://github.com/LPD-EPFL/ robust-collaborative-learning. |
| Researcher Affiliation | Collaboration | 1EPFL, Lausanne, Switzerland. 2Tournesol, 3Calicarpa, Switzerland. Correspondence to: Sadegh Farhadkhani <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 MONNA as executed by a correct node i |
| Open Source Code | Yes | We validate our theory by experiments on image classification and make our code available at https://github.com/LPD-EPFL/ robust-collaborative-learning. |
| Open Datasets | Yes | We use the MNIST (Le Cun & Cortes, 2010) and CIFAR-10 (Krizhevsky et al., 2009) datasets, pre-processed as in (Baruch et al., 2019) and (El Mhamdi et al., 2021b). |
| Dataset Splits | No | The paper mentions using MNIST and CIFAR-10 datasets and discusses data heterogeneity, but it does not explicitly provide details about training, validation, and test splits (e.g., percentages or counts). |
| Hardware Specification | Yes | We list below the hardware components used: 1 Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz, 2 Nvidia Ge Force GTX 1080 Ti, 64 GB of RAM |
| Software Dependencies | Yes | Python 3.8.10 has been used to run our scripts. Besides the standard libraries associated with Python 3.8.10, our scripts use the following libraries: numpy 1.19.1, torch 1.6.0, torchvision 0.7.0, pandas 1.1.0, matplotlib 3.0.2, requests 2.21.0, urllib3 1.24.1, chardet 3.0.4, certifi 2018.08.24, pytz 2020.1, dateutil 2.6.1, pyparsing 2.2.0, cycler 0.10.0, kiwisolver 1.0.1, cffi 1.13.2. |
| Experiment Setup | Yes | For MNIST, we consider a convolutional neural network (CNN)... The model is trained using a learning rate γ = 0.75 for T = 600 iterations. ... For CIFAR-10, we use a CNN... we set γ = 0.5 and T = 2000 iterations. ... Momentum β = 0.99 (except for SCC: β = 0.9), Batch size b = 25 (MNIST), b = 50 (CIFAR-10). |