# On Large-Scale Multiple Testing Over Networks: An Asymptotic Approach

arXiv: https://arxiv.org/abs/2211.16059 · HTML: https://mlsanity.com/paper/2211.16059/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 52 references that are in the map.
- First posted on arXiv 29 Nov 2022.

## Builds on (most cited first, 5 of 5)

- [A unified treatment of multiple testing with prior knowledge using the p-filter](https://mlsanity.com/paper/1703.06222/) (2017)
- [Communication-Efficient Distributed Multiple Testing for Large-Scale Inference](https://mlsanity.com/paper/2201.13008/) (2022)
- [Sample-and-Forward: Communication-Efficient Control of the False Discovery Rate in Networks](https://mlsanity.com/paper/2210.02555/) (2022)
- [QuTE: decentralized multiple testing on sensor networks with false discovery rate control](https://mlsanity.com/paper/2210.04334/) (2022)
- [Multiple Hypothesis Testing Framework for Spatial Signals](https://mlsanity.com/paper/2108.12314/) (2021)

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

- [Decentralized Conformal Novelty Detection via Quantized Model Exchange](https://mlsanity.com/paper/2605.08263/) (2026)

## Abstract (arXiv)

This work concerns developing communication- and computation-efficient methods for large-scale multiple testing over networks, which is of interest to many practical applications. We take an asymptotic approach and propose two methods, proportion-matching and greedy aggregation, tailored to distributed settings. The proportion-matching method achieves the global BH performance yet only requires a one-shot communication of the (estimated) proportion of true null hypotheses as well as the number of p-values at each node. By focusing on the asymptotic optimal power, we go beyond the BH procedure by providing an explicit characterization of the asymptotic optimal solution. This leads to the greedy aggregation method that effectively approximates the optimal rejection regions at each node, while computation efficiency comes from the greedy-type approach naturally. Moreover, for both methods, we provide the rate of convergence for both the FDR and power. Extensive numerical results over a variety of challenging settings are provided to support our theoretical findings.