Low-Rank Optimization for Data Shuffling in Wireless Distributed Computing

Abstract

Wireless distributed computing presents new opportunities to execute intelligent tasks on mobile devices for low-latency applications, by wirelessly aggregating the computation and storage resources among mobile devices. However, for low-latency applications, the key bottleneck lies in the exchange of intermediate results among mobile devices for data shuffling. To improve communication efficiency therein, we establish a novel interference alignment condition by exploiting the locally computed intermediate values as side information. The low-rank optimization model is further developed to maximize the achieved degrees-of-freedom (DoFs). Unfortunately, existing convex relaxation based approach fails to yield satisfied performance due to the poor structure in the formulated low-rank optimization problem, for which we develop a novel difference-of-convex (DC) programming based algorithm. We show that this new approach can significantly improve communication efficiency and the achievable DoF is independent of the number of mobile devices.