Summary of Causal Multi-label Feature Selection in Federated Setting, by Yukun Song et al.
Causal Multi-Label Feature Selection in Federated Setting
by Yukun Song, Dayuan Cao, Jiali Miao, Shuai Yang, Kui Yu
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to multi-label feature selection in a federated setting, where data cannot be centralized. It presents the Federated Causal Multi-label Feature Selection (FedCMFS) algorithm, which consists of three subroutines: FedCFL, FedCFR, and FedCFC. The FedCFL subroutine learns relevant features while preserving privacy; FedCFR recovers missed true relevant features; and FedCFC removes false ones. Experimental results on 8 datasets demonstrate the effectiveness of FedCMFS. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists are trying to solve a big problem with how we process data when it’s spread across many sources, but we can’t combine all that data into one place. They came up with an idea called Federated Causal Multi-label Feature Selection (FedCMFS), which is a way to pick the most important features from data without sharing any of it. It has three parts: one helps learn what’s important, another finds things we might have missed, and the last one gets rid of things that aren’t really useful. They tested it on many different datasets and showed that it works well. |
Keywords
* Artificial intelligence * Feature selection
