# ADAGES: adaptive aggregation with stability for distributed feature selection

arXiv: https://arxiv.org/abs/2007.10776 · HTML: https://mlsanity.com/paper/2007.10776/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2020.

## Built on by (most cited first, 1 of 1)

- [Distributed Sparse Feature Selection in Communication-Restricted Networks](https://mlsanity.com/paper/2111.02802/) (2021)

## Abstract (arXiv)

In this era of "big" data, not only the large amount of data keeps motivating distributed computing, but concerns on data privacy also put forward the emphasis on distributed learning. To conduct feature selection and to control the false discovery rate in a distributed pattern with multi-machines or multi-institutions, an efficient aggregation method is necessary. In this paper, we propose an adaptive aggregation method called ADAGES which can be flexibly applied to any machine-wise feature selection method. We will show that our method is capable of controlling the overall FDR with a theoretical foundation while maintaining power as good as the Union aggregation rule in practice.