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Recently, reducing communication time between machines becomes the main focusof distributed data mining. Previous methods propose to make workers do morecomputation locally before aggregating local solutions in the server such thatfewer communication rounds between server and workers are required. However,these methods do not consider reducing the communication time per round andwork very poor under certain conditions, for example, when there are stragglerproblems or the dataset is of high dimension. In this paper, we target toreduce communication time per round as well as the required communicationrounds. We propose a communication-efficient distributed primal-dual methodwith straggler-agnostic server and bandwidth-efficient workers. We analyze theconvergence property and prove that the proposed method guarantees linearconvergence rate to the optimal solution for convex problems. Finally, weconduct large-scale experiments in simulated and real distributed systems andexperimental results demonstrate that the proposed method is much faster thancompared methods.

Straggler-Agnostic and Communication-Efficient Distributed Primal-Dual Algorithm for High-Dimensional Data Mining