# Communication-Aware Collaborative Learning

arXiv: https://arxiv.org/abs/2012.10569 · HTML: https://mlsanity.com/paper/2012.10569/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [On-Demand Sampling: Learning Optimally from Multiple Distributions](https://mlsanity.com/paper/2210.12529/) (2022)
- [Optimal Multi-Distribution Learning](https://mlsanity.com/paper/2312.05134/) (2023)
- [A Resilient Distributed Boosting Algorithm](https://mlsanity.com/paper/2206.04713/) (2022)
- [On Calibration in Multi-Distribution Learning](https://mlsanity.com/paper/2412.14142/) (2024)

## Abstract (arXiv)

Algorithms for noiseless collaborative PAC learning have been analyzed and optimized in recent years with respect to sample complexity. In this paper, we study collaborative PAC learning with the goal of reducing communication cost at essentially no penalty to the sample complexity. We develop communication efficient collaborative PAC learning algorithms using distributed boosting. We then consider the communication cost of collaborative learning in the presence of classification noise. As an intermediate step, we show how collaborative PAC learning algorithms can be adapted to handle classification noise. With this insight, we develop communication efficient algorithms for collaborative PAC learning robust to classification noise.