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..
Stability and Generalization of Decentralized Stochastic Gradient Descent
Authors: Tao Sun, Dongsheng Li, Bao Wang9756-9764
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We verify our theoretical findings by using a variety of decentralized settings and benchmark machine learning models. |
| Researcher Affiliation | Academia | Tao Sun1, Dongsheng Li1 , and Bao Wang2 1College of Computer, National University of Defense Technology, Changsha, Hunan, China. 2Scientific Computing & Imaging Institute, University of Utah, USA. |
| Pseudocode | Yes | Algorithm 1 Decentralized Stochastic Gradient Descent (DSGD) Require: (αt > 0)t 0, initialization x0 for node i = 1, 2, . . . , m for t = 1, 2, . . . updates local parameter as (3) and (4) xt = 1 m Pm i=1 xt(i) end for end for |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We use the Body Fat dataset (Johnson 1996)... We use the benchmark ijcnn1 dataset(Rennie and Rifkin 2001)... Res Net-20 (He et al. 2016) for CIFAR10 classification (Krizhevsky 2009). |
| Dataset Splits | No | The paper mentions using subsets of data for experiments but does not provide specific training/validation/test dataset splits or cross-validation details for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers). |
| Experiment Setup | Yes | We set the number of nodes m to 10 and conduct two kinds of experiments... We compare the training loss and training accuracy of D-SGD on these two datasets... For the above six graphs, we record the absolute difference in the value of function Φ for a set of learning rate, namely, {0.001, 0.004, 0.016, 0.064}... and set λ = 10−4... 100 epochs are used in the nonconvex test. |