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ICML 2022

A Resilient Distributed Boosting Algorithm

Conference Paper Accepted Paper Artificial Intelligence · Machine Learning

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.

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Context

Venue
International Conference on Machine Learning
Archive span
1993-2025
Indexed papers
16471
Paper id
1097061919629536272