Heterogeneity-aware device selection for efficient federated edge learning

Abstract

Federated learning (FL) combined with mobile edge computing (FEEL) provides an end-to-edge synergetic learning approach to allow end devices to participate in machine learning model training parallelly while ensuring user privacy is maintained. However, conventional FL approaches often overlook two critical characteristics in the real-edge scenario: system heterogeneity and statistical heterogeneity. This oversight can detrimentally impact both the training efficiency and the model's accuracy. Specifically, it brings intolerable training delays and severe training accuracy degradation. To address these issues, this paper proposes a novel Quality-aware online Device Selection (QDS) algorithm. The QDS algorithm leverages a greedy selection method that guarantees the deadline restrictions and reflects upon their historical performance metrics, as indicated by their loss function values from preceding training rounds. This rigorous selection process ensures that the participating device set is optimally positioned to balance the dual objectives of training efficiency and model accuracy. Furthermore, we have developed a training loss-based device selection mechanism, aimed at prioritizing higher-quality devices for early submission of local updates prior to the designated deadline. Experimental findings demonstrate that the proposed QDS significantly enhances both the speed and accuracy of training when contrasted with baseline methods.