ADAGES: adaptive aggregation with stability for distributed feature selection

TL;DR

ADAGES is a flexible, theoretically grounded method for distributed feature selection that effectively controls false discovery rate while maintaining high power across multiple machines or institutions.

Contribution

The paper introduces ADAGES, an adaptive aggregation approach for distributed feature selection that controls FDR and is compatible with any machine-wise selection method.

Findings

Controls overall FDR with theoretical guarantees

Maintains power comparable to Union aggregation

Applicable to any machine-wise feature selection method

Abstract

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.