When UAV Meets Federated Learning: Latency Minimization via Joint Trajectory Design and Resource Allocation

• URL: http://arxiv.org/abs/2412.07428v1
• Date: Tue, 10 Dec 2024 11:39:27 GMT
• Title: When UAV Meets Federated Learning: Latency Minimization via Joint Trajectory Design and Resource Allocation
• Authors: Xuhui Zhang, Wenchao Liu, Jinke Ren, Huijun Xing, Gui Gui, Yanyan Shen, Shuguang Cui,
• Abstract summary: Federated learning (FL) has emerged as a pivotal solution for training machine learning models over wireless networks. We introduce an innovative approach by deploying an unmanned aerial vehicle (UAV) as a mobile FL server to enhance the training process of FL. To improve the overall training efficiency, we formulate a latency problem by jointly optimizing the bandwidth allocation, computing frequencies, transmit power for both the UAV and IoT devices, and the UAV's trajectory.
• Score: 47.20867891501245
• License:
• Abstract: Federated learning (FL) has emerged as a pivotal solution for training machine learning models over wireless networks, particularly for Internet of Things (IoT) devices with limited computation resources. Despite its benefits, the efficiency of FL is often restricted by the communication quality between IoT devices and the central server. To address this issue, we introduce an innovative approach by deploying an unmanned aerial vehicle (UAV) as a mobile FL server to enhance the training process of FL. By leveraging the UAV's maneuverability, we establish robust line-of-sight connections with IoT devices, significantly improving communication capacity. To improve the overall training efficiency, we formulate a latency minimization problem by jointly optimizing the bandwidth allocation, computing frequencies, transmit power for both the UAV and IoT devices, and the UAV's trajectory. Then, an efficient alternating optimization algorithm is developed to solve it efficiently. Furthermore, we analyze the convergence and computational complexity of the proposed algorithm. Finally, numerical results demonstrate that our proposed scheme not only outperforms existing benchmark schemes in terms of latency but also achieves training efficiency that closely approximate the ideal scenario.

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