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.
| Original language | English |
|---|---|
| Pages (from-to) | 6465-6473 |
| Number of pages | 9 |
| Journal | Proceedings of Machine Learning Research |
| Volume | 162 |
| State | Published - 2022 |
| Event | 39th International Conference on Machine Learning, ICML 2022 - Baltimore, United States Duration: 17 Jul 2022 → 23 Jul 2022 https://proceedings.mlr.press/v162/ |
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Software
- Control and Systems Engineering
- Statistics and Probability