The Node Selection Strategy for Federated Learning in UAV-Assisted Edge Computing Environment

Abstract

Recently, edge computing plays the important role in internet of things (IoT). However, for the emergency scenarios, the capacity of edge computing is limited. Thus, the UAV-assisted edge computing is introduced for IoT. Meanwhile, it faces privacy disclosure issue that the the data generated on UTs is transmitted to edge/cloud servers for processing. So, the federated learning (FL) is adopted to train model. But due to the difference between UTs or data quality, and the dynamic wireless network, the FL faces the bottleneck of training efficiency and accuracy in UAV-assisted edge computing environment. In this paper, a three-layer FL architecture is proposed. the FL training process is optimized by jointly optimizing the UT selection, the UAV selection, the data set selection and the wait delay selection of one-time training. The convergence gap optimization problem is built by analyzing model training convergence, and the the node selection algorithm is designed. Finally, the extensive simulation experiments are conducted to verify the feasibility and efficiency of proposed algorithm for FL.