# Communication-Aware Collaborative Learning

@inproceedings{Blum2020CommunicationAwareCL, title={Communication-Aware Collaborative Learning}, author={Avrim Blum and Shelby Heinecke and L. Reyzin}, booktitle={AAAI Conference on Artificial Intelligence}, year={2020} }

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…

## One Citation

### On-Demand Sampling: Learning Optimally from Multiple Distributions

- Computer ScienceArXiv
- 2022

The optimal sample complexity of multi-distribution learning paradigms, such as collaborative, group distributionally robust, and fair federated learning are established and algorithms that meet this sample complexity are given.

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