Before revision:  stochastic gradient evaluations and O( \sqrt{\kappa_b\kappa_c \log1{\epsilon}) communication rounds to reach precision \epsilon, where \kappa_s and \kappa_b are the stochastic condition number and batch condition number for strongly convex and smooth problems,  \kappa_c is the condition number of the communication network, and n is the sample size on each distributed node.
After revision:  stochastic gradient evaluations and O( (\kappa_b+\kappa_c) \log1{\epsilon}) communication rounds to reach precision \epsilon, where \kappa_s and \kappa_b are the stochastic condition number and batch condition number for strongly convex and smooth problems, \kappa_c is the condition number of the communication network, and n is the sample size on each distributed node.