Data-Importance Aware User Scheduling for Communication-Efficient Edge Machine Learning

Understand

With the prevalence of intelligent mobile applications, edge learning is emerging as a promising technology for powering fast intelligence acquisition for edge devices from distributed data generated at the network edge.

• One critical task of edge learning is to efficiently utilize the limited radio resource to acquire data samples for model training at an edge server.
• In this paper, we develop a novel user scheduling algorithm for data acquisition in edge learning, called (data) importance-aware scheduling.
• A key feature of this scheduling algorithm is that it takes into account the informativeness of data samples, besides communication reliability.