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This paper develops an optimal data aggregation policy for learning-basedtraffic control systems based on imagery collected from Road Side Units (RSUs)under imperfect communications. Our focus is optimizing semantic informationflow from RSUs to a nearby edge server or cloud-based processing units bymaximizing data diversity based on the target machine learning applicationwhile taking into account heterogeneous channel conditions (e.g., delay, errorrate) and constrained total transmission rate. As a proof-of-concept, weenforce fairness among class labels to increase data diversity forclassification problems. The developed constrained optimization problem isnon-convex. Hence it does not admit a closed-form solution, and the exhaustivesearch is NP-hard in the number of RSUs. To this end, we propose an approximatealgorithm that applies a greedy interval-by-interval scheduling policy byselecting RSUs to transmit. We use coalition game formulation to maximize theoverall added fairness by the selected RSUs in each transmission interval.Once, RSUs are selected, we employ a maximum uncertainty method to handpickdata samples that contribute the most to the learning performance. Our methodoutperforms random selection, uniform selection, and pure network-basedoptimization methods (e.g., FedCS) in terms of the ultimate accuracy of thetarget learning application.

Diversity Maximized Scheduling in RoadSide Units for Traffic Monitoring Applications