LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning
Authors: Timothy Castiglia, Yi Zhou, Shiqiang Wang, Swanand Kadhe, Nathalie Baracaldo, Stacy Patterson
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide extensive empirical evidence that LESS-VFL can achieve high accuracy and remove spurious features at a fraction of the communication cost of other feature selection approaches. |
| Researcher Affiliation | Collaboration | 1Rensselaer Polytechnic Institute 2IBM Research. |
| Pseudocode | Yes | Algorithm 1 LESS-VFL implemented using P-SGD |
| Open Source Code | No | The paper does not contain an explicit statement about the release of its source code, nor does it provide a link to a code repository. |
| Open Datasets | Yes | MIMIC-III (Johnson et al., 2016; Harutyunyan et al., 2019): Hospital dataset... Activity (Anguita et al., 2013): Time-series positional data... Phishing (Dua & Graff, 2017): Dataset... Gina (Guyon, 2007): Hand-written two-digit images. Sylva (Guyon, 2007): Forest cover type information. |
| Dataset Splits | No | The paper mentions splitting features among parties but does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU or CPU models, memory specifications, or cloud computing instance types. |
| Software Dependencies | No | The paper mentions the ADAM optimizer and P-SGD but does not provide specific version numbers for any software libraries, frameworks, or operating systems used in the experimental setup. |
| Experiment Setup | Yes | We run a grid search to determine regularization parameters for LESS-VFL, local lasso, and group lasso, and the number of pre-training epochs for LESS-VFL and local lasso. We use the ADAM optimizer with a learning rate of 0.01 when employing Algorithm 2 in VFL (Original and Spurious) and pre-training and post feature selection in local lasso and LESS-VFL. We run 150 epochs of P-SGD for embedding component selection in LESS-VFL and feature selection in LESS-VFL and local lasso, which we found to be a sufficient amount of iterations for the training loss to plateau. |