# Distributed Maximum Consensus over Noisy Links

arXiv: https://arxiv.org/abs/2403.18509 · HTML: https://mlsanity.com/paper/2403.18509/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 36 references that are in the map.
- First posted on arXiv 27 Mar 2024.

## Builds on (most cited first, 11 of 11)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Decentralized learning for wireless communications and networking](https://mlsanity.com/paper/1503.08855/) (2015)
- [Secure and Privacy-Preserving Consensus](https://mlsanity.com/paper/1707.04491/) (2017)
- [Dynamic Max-Consensus and Size Estimation of Anonymous Multi-Agent Networks](https://mlsanity.com/paper/2009.03858/) (2020)
- [From Simplicity to Complexity Based on Consensus: A Case Study](https://mlsanity.com/paper/1610.09482/) (2016)
- [MAX-consensus in open multi-agent systems with gossip interactions](https://mlsanity.com/paper/1709.05793/) (2017)
- [Privacy-Preserving Push-sum Average Consensus via State Decomposition](https://mlsanity.com/paper/2009.12029/) (2020)
- [Optimal Periodic Sensor Scheduling in Networks of Dynamical Systems](https://mlsanity.com/paper/1305.5601/) (2013)
- [Max Consensus in Sensor Networks: Non-linear Bounded Transmission and Additive Noise](https://mlsanity.com/paper/1602.01128/) (2016)
- [Analysis and Design of Robust Max Consensus for Wireless Sensor Networks](https://mlsanity.com/paper/1904.09377/) (2019)
- [Dynamic Median Consensus Over Random Networks](https://mlsanity.com/paper/2110.05317/) (2021)

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

- [Noise-Robust and Resource-Efficient ADMM-based Federated Learning](https://mlsanity.com/paper/2409.13451/) (2024)

## Abstract (arXiv)

We introduce a distributed algorithm, termed noise-robust distributed maximum consensus (RD-MC), for estimating the maximum value within a multi-agent network in the presence of noisy communication links. Our approach entails redefining the maximum consensus problem as a distributed optimization problem, allowing a solution using the alternating direction method of multipliers. Unlike existing algorithms that rely on multiple sets of noise-corrupted estimates, RD-MC employs a single set, enhancing both robustness and efficiency. To further mitigate the effects of link noise and improve robustness, we apply moving averaging to the local estimates. Through extensive simulations, we demonstrate that RD-MC is significantly more robust to communication link noise compared to existing maximum-consensus algorithms.