COKE: Communication-Censored Decentralized Kernel Learning
Authors: Ping Xu, Yue Wang, Xiang Chen, Zhi Tian
JMLR 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive tests on both synthetic and real datasets are conducted to verify the communication efficiency and learning effectiveness of COKE.1 |
| Researcher Affiliation | Academia | Ping Xu EMAIL Yue Wang EMAIL Xiang Chen EMAIL Zhi Tian EMAIL Department of Electrical and Computer Engineering, George Mason University Fairfax, VA 22030, USA |
| Pseudocode | Yes | Algorithm 1 DKLA Run at Agent i |
| Open Source Code | No | The paper does not provide explicit information about the availability of open-source code for the methodology described. |
| Open Datasets | Yes | To further evaluate our algorithms, the following popular real-world datasets from UCI machine learning repository are chosen (Asuncion and Newman, 2007). |
| Dataset Splits | Yes | each agent uses 70% of its data for training and the rest for testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run its experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | The censoring thresholds are h(k) = 0.95k, the regularization parameter λ and stepsize ρ of DKLA and COKE are set to be 5 10 5 and 10 2, respectively. The stepsize of CTA is set to be η = 0.99 |