1. Joint Optimization of UAV Trajectory and Resource Allocation for Federal Learning.
- Author
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YAO Xiancai, ZHENG Jianchao, ZHENG Xin, and YANG Xiaolong
- Subjects
TRAJECTORY optimization ,OPTIMIZATION algorithms ,FEDERATED learning ,RESOURCE allocation ,NONLINEAR programming ,ENERGY consumption - Abstract
Unmanned aerial vehicles (UAVs), due to their mobility and flexibility, are widely used in tasks such as search and tracking. The large amount of data generated during task execution can be significantly leveraged by machine learning (ML) algorithms to improve the intelligence of UAV clusters. Federated learning (FL), as a distributed machine learning architecture, is more suitable for UAV networks with limited bandwidth and energy budget, as it only requires model parameter transmission during training. To fully exploit the advantages of FL in UAV networks, this paper models factors affecting training energy consumption, such as bandwidth, computational frequency, FL accuracy, and training delay. A joint optimization algorithm is proposed to minimize the overall training energy consumption of FL by jointly optimizing training parameter settings, UAV trajectories, resource allocation of communication and computation in UAV networks. The proposed joint optimization algorithm decomposes the mixed integer nonlinear integer programming problem (MINLP) into three sub-problems, and transforms the non-convex sub-problems into convex sub-problems through methods such as successive convex approximation (SCA) and block coordinate descent to obtain suboptimal solutions. Simulation results show that, based on the constraints of training accuracy, UAV movement, and FL delay, the proposed algorithm reduces the overall training energy consumption of unmanned aerial vehicles by more than 15% compared to the existing joint training and resource optimization schemes. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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