无人机辅助的高能效边缘联邦学习综述

doi:10.11896/jsjkx.231100084

Abstract

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

CLC Number:

TN929

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.

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