Energy-Efficient Federated Learning Through UAV Edge Under Location Uncertainties

IF 6.7 2区 计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY
Chen Wang;Xiao Tang;Daosen Zhai;Ruonan Zhang;Nurzhan Ussipov;Yan Zhang
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Abstract

Federated Learning (FL) and Mobile Edge Computing (MEC) technologies alleviate the burden of deploying artificial intelligence (AI) on wireless devices with low computational capabilities. However, they also introduce energy consumption challenges in FL model training and data processing. In this paper, we employ Unmanned Aerial Vehicles (UAVs) to collect data from wireless devices and carry edge servers to assist the central server located at the base station in training FL model. We also consider the deviation of UAVs' locations to address its impact on network performance. Specifically, we formulate a robust joint optimization problem to minimize the energy consumption of UAVs, considering the computational resources, transmit power, transmission time, and FL model accuracy. Moreover, Gaussian-distributed uncertainties caused by deviation in UAV locations result in probabilistic constraints on data offloading. We initially employ the Bernstein-type inequality (BTI) to transform probabilistic constraints into deterministic forms. Subsequently, we adopt the Block Coordinate Descent (BCD) to separate the problem into three subproblems. Simulation results demonstrate a significant reduction in energy consumption and superiority in robustness.
位置不确定下无人机边缘节能联邦学习
联邦学习(FL)和移动边缘计算(MEC)技术减轻了在计算能力较低的无线设备上部署人工智能(AI)的负担。然而,它们也在FL模型训练和数据处理中引入了能耗挑战。在本文中,我们使用无人机(uav)从无线设备收集数据,并携带边缘服务器,以协助位于基站的中央服务器训练FL模型。我们还考虑了无人机位置的偏差,以解决其对网络性能的影响。具体而言,考虑计算资源、发射功率、发射时间和FL模型精度,提出了一种鲁棒联合优化问题,以最小化无人机的能量消耗。此外,无人机位置偏差引起的高斯分布不确定性导致数据卸载的概率约束。我们最初采用伯恩斯坦型不等式(BTI)将概率约束转换为确定性形式。随后,我们采用分块坐标下降法(BCD)将问题分解为三个子问题。仿真结果表明,该方法显著降低了能耗,具有较好的鲁棒性。
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来源期刊
IEEE Transactions on Network Science and Engineering
IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering
CiteScore
12.60
自引率
9.10%
发文量
393
期刊介绍: The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.
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