  This paper develops an optimal data aggregation policy for learning-based
traffic control systems based on imagery collected from Road Side Units (RSUs)
under imperfect communications. Our focus is optimizing semantic information
flow from RSUs to a nearby edge server or cloud-based processing units by
maximizing data diversity based on the target machine learning application
while taking into account heterogeneous channel conditions (e.g., delay, error
rate) and constrained total transmission rate. As a proof-of-concept, we
enforce fairness among class labels to increase data diversity for
classification problems. The developed constrained optimization problem is
non-convex. Hence it does not admit a closed-form solution, and the exhaustive
search is NP-hard in the number of RSUs. To this end, we propose an approximate
algorithm that applies a greedy interval-by-interval scheduling policy by
selecting RSUs to transmit. We use coalition game formulation to maximize the
overall added fairness by the selected RSUs in each transmission interval.
Once, RSUs are selected, we employ a maximum uncertainty method to handpick
data samples that contribute the most to the learning performance. Our method
outperforms random selection, uniform selection, and pure network-based
optimization methods (e.g., FedCS) in terms of the ultimate accuracy of the
target learning application.
