Communication-Aware Collaborative Learning [article]

Avrim Blum, Shelby Heinecke, Lev Reyzin
2020 arXiv   pre-print
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
more » ... termediate 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.
arXiv:2012.10569v1 fatcat:ps5lh4yw25aajebz5s73iw4hpe