Communication-Efficient Frank-Wolfe Algorithm for Nonconvex Decentralized Distributed Learning
Authors: Wenhan Xian, Feihu Huang, Heng Huang10405-10413
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on matrix completion and model compression applications demonstrate the efficiency of our new algorithm. |
| Researcher Affiliation | Collaboration | Wenhan Xian 1, Feihu Huang 1, Heng Huang 1,2 1 Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, USA 2 JD Finance America Corporation, Mountain View, CA, USA |
| Pseudocode | Yes | Algorithm 1 Decentralized Quantized Stochastic Frank Wolfe (DQSFW) |
| Open Source Code | No | The paper does not contain any statement about making its source code available or provide a link to a code repository. |
| Open Datasets | Yes | We run our experiment on two benchmark datasets, Movie Lens 100k and Movie Lens 1m (Harper and Konstan 2015). |
| Dataset Splits | No | For Movie Lens datasets, the paper states, 'we take all data for training'. For CIFAR-10, it mentions '50000 training samples' but does not specify a validation split or percentages for train/test/validation. |
| Hardware Specification | Yes | Each node is an Intel Xeon E5-2660 machine within an infiniband network. ... The experiment is implemented on 8 GTX1080 GPUs by Pytorch. |
| Software Dependencies | No | The paper mentions 'mpi4py' and 'Pytorch' as software used, but it does not specify version numbers for these dependencies. |
| Experiment Setup | Yes | For all of the three algorithms, we set step size ηt = t 0.75. ... For all three algorithms, step size is chosen as ηt = 1 2t 0.75. ... we use cross-entropy loss function as the criterion and set τ = 0.8. |