无人机辅助的高能效边缘联邦学习综述

doi:10.11896/jsjkx.231100084

摘要/Abstract

摘要： 随着移动通信技术的快速发展和物联网终端设备数量激增,丰富多样的智能应用及海量数据在网络边缘产生,边缘智能应运而生。当前,联邦学习作为一种新兴的分布式机器学习方法,可以在不共享终端设备原始数据的情况下协作完成模型训练任务,是实现边缘智能的重要方式。传统的边缘智能网络以地面通信基站为参数服务器,其服务范围相对固定,无法适应复杂多变的异构网络环境。无人机由于其灵活性和机动性被引入联邦学习中,可以有效地在边缘智能网络中提供通信/计算/缓存服务,增强地面网络的通信容量,弥补传统地面网络通信范围受限、通信开销大、数据传输延迟高等缺点。无人机辅助的联邦学习具有通信覆盖范围广、通信开销低、即时响应等明显优势,同时也面临通信带宽受限、不可靠的通信环境、飞行环境的不确定性等挑战,上述挑战可能导致低能效问题。无人机辅助的高能效边缘联邦学习是将无人机作为边缘服务器的计算能耗、计算频率、时间分配等纳入考虑,研究无人机辅助联邦学习系统的能效优化方案。针对无人机作为边缘服务器这一场景,依据最小化能耗、最小化延迟和最小化能耗延迟加权和等不同的优化目标,对当前无人机辅助的高能效边缘联邦学习研究进行了分类和总结,并对未来研究方向进行了思考和展望。

关键词: 联邦学习, 无人机, 能效优化, 边缘智能, 无线网络

Abstract: With the rapid development of mobile communication technology and the proliferation of Internet of Things(IoT) terminal devices,rich and diverse intelligent applications and massive data are generated at the edge of the network,and edge intelligence applications are born.Currently,as an emerging distributed machine learning method,federated learning can collaborate to complete the model training task without sharing the raw data of terminal devices,which is an important way to achieve edge intelligence.The traditional edge intelligence network uses the ground communication base station as the parameter server,and its service range is relatively fixed,which cannot adapt to the complex and changing heterogeneous network environment.Unmanned aerial vehicles(UAVs) introduced into federated learning due to their flexibility and mobility,so as to effectively provide communication/computation/caching services in edge intelligence networks,enhance the communication capacity of the ground network,and make up for the shortcomings of the traditional ground network such as limited communication range,high communication overhead,and high data transmission delay.UAV-assisted federated learning has obvious advantages such as wide communication coverage,low communication overhead,and instant response,but it also faces challenges such as limited communication bandwidth,unreliable communication environment,and uncertainty of flight environment,and the above challenges may lead to low energy efficiency problems.UAV-assisted energy efficient edge federated learning is to study the energy efficiency optimization scheme by considering the computational energy consumption,computational frequency and time allocation of UAVs as edge ser-vers.For the scenario of UAVs as edge servers,the current research on UAV-assisted energy-efficient federated learning is classified and summarized on the basis of different optimization objectives,such as minimizing energy consumption,minimizing latency,and minimizing energy-delay weighted sums,and the future research directions are considered and outlooked.

Key words: Federated learning, Unmanned aerial vehicle, Energy-Efficient optimization, Edge intelligence, Wireless network

中图分类号:

TN929

卢彦丰, 吴韬, 刘春生, 颜康, 屈毓锛. 无人机辅助的高能效边缘联邦学习综述[J]. 计算机科学, 2024, 51(4): 270-279. https://doi.org/10.11896/jsjkx.231100084

LU Yanfeng, WU Tao, LIU Chunsheng, YAN Kang, QU Yuben. Survey of UAV-assisted Energy-Efficient Edge Federated Learning[J]. Computer Science, 2024, 51(4): 270-279. https://doi.org/10.11896/jsjkx.231100084

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