Reviews: An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums

Optimization under communication constraints is an important area of machine learning and there is still much to be gained from rigorous research in this area. Statistical literature does not concern itself much with inter- and intra-processor communication; nor does optimization (operation research) literature. Machine learning is a natural community for this research to be advanced. This paper uses a randomization scheme for communicating information between nodes to achieve impressive learning rates. The authors derive theoretical bounds on the estimation error induced by the communication constraint and show that they achieve improvements over state-of-the-art approaches with some empirical experiments.