# Sample-and-Forward: Communication-Efficient Control of the False Discovery Rate in Networks

arXiv: https://arxiv.org/abs/2210.02555 · HTML: https://mlsanity.com/paper/2210.02555/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 28 references that are in the map.
- First posted on arXiv 5 Oct 2022.

## Builds on (most cited first, 2 of 2)

- [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)

## Built on by (most cited first, 2 of 2)

- [On Large-Scale Multiple Testing Over Networks: An Asymptotic Approach](https://mlsanity.com/paper/2211.16059/) (2022)
- [On the Adversarial Robustness of Learning-based Conformal Novelty Detection](https://mlsanity.com/paper/2510.00463/) (2025)

## Abstract (arXiv)

This work concerns controlling the false discovery rate (FDR) in networks under communication constraints. We present sample-and-forward, a flexible and communication-efficient version of the Benjamini-Hochberg (BH) procedure for multihop networks with general topologies. Our method evidences that the nodes in a network do not need to communicate p-values to each other to achieve a decent statistical power under the global FDR control constraint. Consider a network with a total of m p-values, our method consists of first sampling the (empirical) CDF of the p-values at each node and then forwarding O(log m) bits to its neighbors. Under the same assumptions as for the original BH procedure, our method has both the provable finite-sample FDR control as well as competitive empirical detection power, even with a few samples at each node. We provide an asymptotic analysis of power under a mixture model assumption on the p-values.