  Intelligent mobile platforms such as smart vehicles and drones have recently
become the focus of attention for onboard deployment of machine learning
mechanisms to enable low latency decisions with low risk of privacy breach.
However, most such machine learning algorithms are both computation-and-memory
intensive, which makes it highly difficult to implement the requisite
computations on a single device of limited computation, memory, and energy
resources. Wireless distributed computing presents new opportunities by pooling
the computation and storage resources among devices. For low-latency
applications, the key bottleneck lies in the exchange of intermediate results
among mobile devices for data shuffling. To improve communication efficiency,
we propose a co-channel communication model and design transceivers by
exploiting the locally computed intermediate values as side information. A
low-rank optimization model is proposed to maximize the achieved
degrees-of-freedom (DoF) by establishing the interference alignment condition
for data shuffling. Unfortunately, existing approaches to approximate the rank
function fail to yield satisfactory performance due to the poor structure in
the formulated low-rank optimization problem. In this paper, we develop an
efficient DC algorithm to solve the presented low-rank optimization problem by
proposing a novel DC representation for the rank function. Numerical
experiments demonstrate that the proposed DC approach can significantly improve
the communication efficiency whereas the achievable DoF almost remains
unchanged when the number of mobile devices grows.
