A resilient distributed boosting algorithm

Yuval Filmus, Idan Mehalel and Shay Moran
ICML 2022

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.


  title = {A resilient distributed boosting algorithm},
  author = {Yuval Filmus and Idan Mehalel and Shay Moran},
  booktitle = {ICML'22},
  year = {2022}
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