  Edge intelligence requires to fast access distributed data samples generated
by edge devices. The challenge is using limited radio resource to acquire
massive data samples for training machine learning models at edge server. In
this article, we propose a new communication-efficient edge intelligence scheme
where the most useful data samples are selected to train the model. Here the
usefulness or values of data samples is measured by data diversity which is
defined as the difference between data samples. We derive a close-form
expression of data diversity that combines data informativeness and channel
quality. Then a joint data-and-channel diversity aware multiuser scheduling
algorithm is proposed. We find that noise is useful for enhancing data
diversity under some conditions.
