A Resilient Distributed Boosting Algorithm
Abstract
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 (Impagliazzo, 1995), 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.
Cite
Text
Filmus et al. "A Resilient Distributed Boosting Algorithm." International Conference on Machine Learning, 2022.Markdown
[Filmus et al. "A Resilient Distributed Boosting Algorithm." International Conference on Machine Learning, 2022.](https://mlanthology.org/icml/2022/filmus2022icml-resilient/)BibTeX
@inproceedings{filmus2022icml-resilient,
title = {{A Resilient Distributed Boosting Algorithm}},
author = {Filmus, Yuval and Mehalel, Idan and Moran, Shay},
booktitle = {International Conference on Machine Learning},
year = {2022},
pages = {6465-6473},
volume = {162},
url = {https://mlanthology.org/icml/2022/filmus2022icml-resilient/}
}