Recovering Exact Support in Federated lasso without Optimization
Authors: Adarsh Barik, Jean Honorio
TMLR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we validate our theoretical results by conducting computational experiments. |
| Researcher Affiliation | Academia | Adarsh Barik EMAIL Institute of Data Science National University of Singapore Jean Honorio EMAIL School of Computing and Information Systems The University of Melbourne |
| Pseudocode | Yes | Algorithm 1: get Exact Support |
| Open Source Code | No | The paper does not provide any explicit statement or link to source code for the described methodology. |
| Open Datasets | Yes | We used the Blog Feedback data set (Buza, K., 2014) from https://archive.ics.uci.edu/ml/datasets/Blog Feedback. |
| Dataset Splits | Yes | We divided data into training and test data by choosing 80% of all samples to be training data at random. |
| Hardware Specification | No | The paper mentions that the method can run on "devices with low computational capabilities" but does not specify the hardware used for the experiments reported in the paper. |
| Software Dependencies | No | The paper does not explicitly mention any specific software components with version numbers used for the experiments. |
| Experiment Setup | Yes | The number of samples per client ni is varied with control parameter C as 10Cs2 log s. The number of clients g is varied with control parameter C as 10C log d. We conducted our experiment using 0.08. |