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..
On the Convergence of Communication-Efficient Local SGD for Federated Learning
Authors: Hongchang Gao, An Xu, Heng Huang7510-7518
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | At last, extensive experiments are conducted to verify the performance of our proposed methods. and Extensive experimental results con๏ฌrmed the effectiveness of our proposed methods. |
| Researcher Affiliation | Collaboration | 1 Department of Computer and Information Sciences, Temple University, PA, USA 2 Department of Electrical and Computer Engineering, University of Pittsburgh, PA, USA 3 JD Finance America Corporation, Mountain View, CA, USA |
| Pseudocode | Yes | Algorithm 1 Local SGD with Compressed Gradients and Algorithm 2 Momentum Local SGD with Compressed Gradients |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | CIFAR-10: We test Res Net-56 (He et al. 2016) with all the above mentioned algorithms on CIFAR-10 dataset (Krizhevsky, Hinton et al. 2009)., Image Net: We test Res Net-50 (He et al. 2016) on Image Net dataset (Russakovsky et al. 2015) 2. |
| Dataset Splits | No | The paper mentions training and testing but does not specify explicit train/validation/test dataset splits by percentage, count, or a reference to predefined splits. |
| Hardware Specification | Yes | All experiments are implemented in Py Torch (Paszke et al. 2019) and run on a cluster with NVIDIA Tesla P40 GPUs, where nodes are interconnected by a network with 40 Gbps bandwidth. |
| Software Dependencies | No | The paper mentions 'Py Torch (Paszke et al. 2019)' but does not specify its version number or any other software dependencies with versions. |
| Experiment Setup | Yes | The base learning rate is 0.1, the weight decay is 5 10 4 and the total batch size is 128. For local SGD, the model is trained for 150 epoch in total, with a learning rate decay of 0.1 at epoch 100. For momentum local SGD, the model is trained for 200 epoch in total, with a learning rate decay of 0.1 at epoch 100 and 150. |