# Robust Regression with Ensembles Communicating over Noisy Channels

arXiv: https://arxiv.org/abs/2408.10942 · HTML: https://mlsanity.com/paper/2408.10942/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 20 Aug 2024.

## Builds on (most cited first, 6 of 6)

- [Distributed Deep Neural Networks over the Cloud, the Edge and End Devices](https://mlsanity.com/paper/1709.01921/) (2017)
- [Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach](https://mlsanity.com/paper/2102.04170/) (2021)
- [AI and Memory Wall](https://mlsanity.com/paper/2403.14123/) (2024)
- [On the folded normal distribution](https://mlsanity.com/paper/1402.3559/) (2014)
- [Over-the-Air Computation Systems: Optimization, Analysis and Scaling Laws](https://mlsanity.com/paper/1909.00329/) (2019)
- [Robust Nonparametric Regression under Huber's ε-contamination Model](https://mlsanity.com/paper/1805.10406/) (2018)

## Abstract (arXiv)

As machine-learning models grow in size, their implementation requirements cannot be met by a single computer system. This observation motivates distributed settings, in which intermediate computations are performed across a network of processing units, while the central node only aggregates their outputs. However, distributing inference tasks across low-precision or faulty edge devices, operating over a network of noisy communication channels, gives rise to serious reliability challenges. We study the problem of an ensemble of devices, implementing regression algorithms, that communicate through additive noisy channels in order to collaboratively perform a joint regression task. We define the problem formally, and develop methods for optimizing the aggregation coefficients for the parameters of the noise in the channels, which can potentially be correlated. Our results apply to the leading state-of-the-art ensemble regression methods: bagging and gradient boosting. We demonstrate the effectiveness of our algorithms on both synthetic and real-world datasets.