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..
Towards Faster Decentralized Stochastic Optimization with Communication Compression
Authors: Rustem Islamov, Yuan Gao, Sebastian Stich
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide numerical experiments to validate our theoretical findings and confirm the practical superiority of Mo TEF. ... 4 NUMERICAL EXPERIMENTS In this section, we complement the theoretical results on the convergence of Algorithm 1 with numerical evaluations. |
| Researcher Affiliation | Academia | Rustem Islamov1 Yuan Gao2,3 Sebastian U. Stich3 1Universität Basel, 2Universität des Saarlandes, 3CISPA Helmholtz Center for Information Security EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Mo TEF ... Algorithm 2 Mo TEF-VR |
| Open Source Code | Yes | Our implementation is based on open-source code from (Zhao et al., 2022) https://github.com/ liboyue/beer and is available at https://anonymous.4open.science/r/Mo TEF-0DCF. The code to reproduce our synthetic experiment is available at https:// anonymous.4open.science/r/decentralized-exp-A3C6 |
| Open Datasets | Yes | We set λ = 0.05, n = 100 and use Lib SVM datasets (Chang & Lin, 2011). ... Finally, we consider training MLP on MNIST dataset (Deng, 2012) with 1 hidden layer of size 32. |
| Dataset Splits | No | We do not shuffle datasets to have a more heterogeneous setting. Besides, each dataset is equally distributed among all clients. ... Finally, we consider training MLP on MNIST dataset (Deng, 2012) with 1 hidden layer of size 32. The paper does not provide explicit details about training/test/validation splits (e.g., percentages, sample counts, or citations to specific standard splits used in their experimental setup) for the datasets mentioned. |
| Hardware Specification | Yes | We run our experiments on AMD EPYC 9554 64-Core Processor. |
| Software Dependencies | Yes | To find a suitable choice of constants we use Symbolic Math Toolbox in MATLAB (Inc., 2023). Our code can be found at https://anonymous.4open.science/r/dec-symb-verification. |
| Experiment Setup | Yes | We fix the parameters γ = 0.1, η = 0.0005, λ = 0.005, and n = 16, if the opposite is not stated. ... For Mo TEF we tune stepsize as follows η {0.001, 0.01, 0.05}, γ {0.1, 0.2, 0.5, 0.9}, λ {0.005, 0.01, 0.05, 0.1}. |