A Resilient Distributed Boosting Algorithm
Machine Learning
2022-06-14 v2 Distributed, Parallel, and Cluster Computing
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 [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.
Cite
@article{arxiv.2206.04713,
title = {A Resilient Distributed Boosting Algorithm},
author = {Yuval Filmus and Idan Mehalel and Shay Moran},
journal= {arXiv preprint arXiv:2206.04713},
year = {2022}
}