Energy Consumption Minimization for Hybrid Federated Learning and Offloadable Tasks in UAV-Enabled WPCN

In recent years, federated learning (FL) has been adopted in mobile edge computing (MEC) to protect user privacy. However, in some cases, in addition to performing FL to process private data, users also need to process other non-private data. Therefore, how to integrate private data and non-private data in a MEC system for comprehensive processing is an issue worth studying. In this paper, we propose an unmanned aerial vehicle (UAV)-enabled wireless powered communication network (WPCN) to process both FL tasks and offloadable MEC tasks of UEs, where the UAV charges UEs via wireless power transfer (WPT) technology, executes offloaded tasks, and aggregates the FL model parameters. To minimize energy consumption of the UAV, we formulate a problem to jointly optimize the hovering position of UAV, the WPT power, the proportion of UAV's computing resources, the percentage of offloaded tasks, and the time scheduling of FL under the energy harvesting constraint. The problem is divided into three subproblems with the aid of block coordinate descent (BCD) method. These subproblems are solved by Lagrange method and heuristic algorithm respectively. Numerical results show our algorithm can reduce the energy consumption of the UAV with low time complexity compared with several benchmarks.