摘要 : 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 unmanned aerial vehicle (UAV)-assisted edge computing is int... 展开 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 unmanned aerial vehicle (UAV)-assisted edge computing is introduced for IoT. Meanwhile, it faces privacy disclosure issues that the data generated on user terminals (UTs) is transmitted to edge/cloud servers for processing. So, the federated learning (FL) is adopted to train the 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 a UAV-assisted edge computing environment. In this article, 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 node selection algorithm is designed. Finally, the extensive simulation experiments are conducted to verify the feasibility and efficiency of the proposed algorithm for FL. 收起