| dc.description.abstract | In modern unmanned aerial vehicle (UAV) systems, the integration of machine learning technology has become increasingly crucial for achieving autonomy, intelligence, and efficiency. Its applications play a critical role in various aspects of life, including surveillance, search and rescue, agriculture, environmental monitoring, and more. However, the training of machine learning models requires a substantial amount of data, and UAVs may be dispersed across different locations, making it challenging to easily transmit data to central training servers. As a result, UAV systems often face challenges related to data privacy and bandwidth limitations. Federated Learning (FL) is an innovative technology that effectively addresses these challenges by leveraging the characteristics of distributed learning. It elevates the performance of UAV systems to new levels, overcoming issues related to data privacy and bandwidth constraints.
This research considers designing a time-slotted model to constrain the behavior of UAV and multiple base stations in a scenario involving a single UAV and multiple base stations. The base stations act as FL clients for training local models, while the UAV serves as the data aggregation center. The objective function is to minimize the overall system energy consumption, subject to a certain level of model accuracy. The optimization involves joint design of UAV flight trajectories, UAV-to-ground station channel gains, ground station transmission power control, and data size allocation. This joint design poses a highly challenging non-convex problem. To overcome the difficulties in this design, this paper employs a method of successive convex approximation (SCA), transforming the problem into a convex one and subsequently utilizing convex optimization for solution. Additionally, the paper proposes an asymptotic solution for UAV flight trajectories, simplifying the optimization problem. It proves that the energy consumption over extended mission times approximates the optimal solution proposed in this paper. The application of FL combined with a single UAV system for joint design convex optimization not only addresses data privacy concerns but also reduces energy consumption during model training, thereby further minimizing the overall system energy consumption. | en_US |