Communication-Aware Collaborative Learning


  • Avrim Blum Toyota Technological Institute at Chicago
  • Shelby Heinecke Salesforce Research
  • Lev Reyzin University of Illinois at Chicago


Learning Theory


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.




How to Cite

Blum, A., Heinecke, S., & Reyzin, L. (2021). Communication-Aware Collaborative Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 35(8), 6786-6793. Retrieved from



AAAI Technical Track on Machine Learning I