Abstract: Privacy-preserving collaborative learning allows multiple data-owners to jointly train machine learning models while keeping their individual datasets private from each other. The main bottleneck against the scalability of such systems to a large number of participants is their communication cost. In this talk, we will introduce novel distributed training frameworks that can achieve scalability...