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

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2024-07-03 DOI:10.1109/TNSE.2024.3422658

Qiang Tang;Yong Yang;Halvin Yang;Dun Cao;Kun Yang

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Abstract

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.

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无人机支持的 WPCN 中混合联合学习和可卸载任务的能耗最小化

近年来，移动边缘计算（MEC）采用联合学习（FL）来保护用户隐私。然而，在某些情况下，用户除了执行联合学习处理隐私数据外，还需要处理其他非隐私数据。因此，如何在 MEC 系统中整合隐私数据和非隐私数据进行综合处理是一个值得研究的问题。在本文中，我们提出了一种支持无人飞行器（UAV）的无线供电通信网络（WPCN）来同时处理 FL 任务和 UE 的可卸载 MEC 任务，其中无人飞行器通过无线供电传输（WPT）技术为 UE 充电，执行卸载任务，并汇总 FL 模型参数。为了使无人机的能耗最小化，我们提出了一个问题，在能量收集约束条件下，联合优化无人机的悬停位置、WPT 功率、无人机计算资源比例、卸载任务比例以及 FL 的时间调度。借助块坐标下降（BCD）方法，该问题被分为三个子问题。这些子问题分别用拉格朗日法和启发式算法求解。数值结果表明，与几个基准相比，我们的算法能以较低的时间复杂度降低无人机的能耗。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

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.