A Resilient Distributed Boosting Algorithm

@InProceedings{pmlr-v162-filmus22a,
  title = 	 {A Resilient Distributed Boosting Algorithm},
  author =       {Filmus, Yuval and Mehalel, Idan and Moran, Shay},
  booktitle = 	 {Proceedings of the 39th International Conference on Machine Learning},
  pages = 	 {6465--6473},
  year = 	 {2022},
  editor = 	 {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
  volume = 	 {162},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {17--23 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v162/filmus22a/filmus22a.pdf},
  url = 	 {https://proceedings.mlr.press/v162/filmus22a.html},
  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.}
}