Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization
Authors: Zhize Li, Dmitry Kovalev, Xun Qian, Peter Richtarik
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct several experiments on real-world datasets which corroborate our theoretical results and confirm the practical superiority of our accelerated methods. |
| Researcher Affiliation | Academia | 1King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia. |
| Pseudocode | Yes | Algorithm 1 Accelerated CGD (ACGD) and Algorithm 2 Accelerated DIANA (ADIANA) |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. No mention of code availability in supplementary materials or a repository link was found. |
| Open Datasets | Yes | In our experiments we use four standard datasets, namely, a5a, mushrooms, a9a and w6a from the LIBSVM library. Some of the experiments are provided in the appendix. |
| Dataset Splits | No | The paper mentions using 'standard datasets' from the LIBSVM library but does not explicitly provide specific train/validation/test dataset splits (e.g., percentages, sample counts, or references to predefined splits within the paper). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory specifications, or cloud instance types) used for running its experiments. It only mentions 'The default number of nodes/machines is 20'. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., library names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | In our experiments, we use the theoretical stepsize and parameters for all the three algorithms: vanilla distributed compressed gradient descent (DCGD), DIANA (Mishchenko et al., 2019), and our ADIANA (Algorithm 2). The default number of nodes/machines is 20 and the regularization parameter λ = 10−3. |