In recent years, a distributed machine learning framework called federated learning (FL) has received much attention. However, in order to ensure the accuracy of the training model, the user device needs to perform multiple rounds of local computation and constantly communicate with the central server to update the model, thereby consuming a significant amount of energy. To reduce energy consumption at user devices, this paper proposes a hierarchical federated learning (HFL) framework assisted by unmanned aerial vehicles (UAVs), where UAVs are employed as edge aggregators to receive and aggregate the models from user devices and relay the updated model to the data center for global aggregation after certain rounds of local training and edge aggregation. The UAV-assisted HFL problem is constructed as a nonlinear mixed integer programming (MIP) problem that aims to minimize the user device energy consumption by jointly optimizing the user-UAV association, the UAVs’ positions, and the transmit power at each user device. Subsequently, an iterative algorithm based on block coordinate descent (BCD) is proposed to find the optimal solution. Simulation results demonstrate that the proposed UAV-assisted HFL method can significantly reduce the total energy consumption at user devices while ensuring the training accuracy.