# A Resilient Distributed Boosting Algorithm

arXiv: https://arxiv.org/abs/2206.04713 · HTML: https://mlsanity.com/paper/2206.04713/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 9 Jun 2022.

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

- [Private Hypothesis Selection](https://mlsanity.com/paper/1905.13229/) (2019)
- [On Communication Complexity of Classification Problems](https://mlsanity.com/paper/1711.05893/) (2017)
- [The Communication Complexity of Optimization](https://mlsanity.com/paper/1906.05832/) (2019)
- [Communication Efficient Distributed Agnostic Boosting](https://mlsanity.com/paper/1506.06318/) (2015)
- [Communication-Aware Collaborative Learning](https://mlsanity.com/paper/2012.10569/) (2020)
- [Convex Set Disjointness, Distributed Learning of Halfspaces, and LP Feasibility](https://mlsanity.com/paper/1909.03547/) (2019)

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

- [A Survey From Distributed Machine Learning to Distributed Deep Learning](https://mlsanity.com/paper/2307.05232/) (2023)

## Abstract (arXiv)

Given a learning task where the data is distributed among several parties, communication is one of the fundamental resources which the parties would like to minimize. We present a distributed boosting algorithm which is resilient to a limited amount of noise. Our algorithm is similar to classical boosting algorithms, although it is equipped with a new component, inspired by Impagliazzo's hard-core lemma [Impagliazzo95], adding a robustness quality to the algorithm. We also complement this result by showing that resilience to any asymptotically larger noise is not achievable by a communication-efficient algorithm.