  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.
