A Resilient Distributed Boosting Algorithm

TL;DR

This paper introduces a distributed boosting algorithm that is resilient to limited noise, reducing communication costs in multi-party learning tasks, and proves limitations for higher noise levels.

Contribution

It presents a novel resilient distributed boosting algorithm inspired by hard-core lemma, addressing noise robustness and communication efficiency.

Findings

Algorithm is resilient to limited noise

Resilience to larger noise levels is impossible with communication efficiency

Provides theoretical bounds on noise tolerance

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.