UAV-Enabled Federated Learning in Dynamic Environments: Efficiency and Security Trade-Off

doi:10.1109/TVT.2023.3347912

	UAV-Enabled Federated Learning in Dynamic Environments: Efficiency and Security Trade-Off
	Fan, Xiaokun 1,2; Chen, Yali 1; Liu, Min 1,2; Sun, Sheng 1; Liu, Zhuotao 3; Xu, Ke 4,5; Li, Zhongcheng 1,2
	2024-05-01
发表期刊	IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
ISSN	0018-9545
卷号	73 期号:5 页码:6993-7006
摘要	Unmanned aerial vehicles (UAVs) can be deployed as flying base stations to provide wireless communication and machine learning (ML) training services for ground user equipments (UEs). Due to privacy concerns, many UEs are not willing to send their raw data to the UAV for model training. Fortunately, federated learning (FL) has emerged as an effective solution to privacy-preserving ML. To balance efficiency and wireless security, this paper proposes a novel secure and efficient FL framework in UAV-enabled networks. Specifically, we design a secure UE selection scheme based on the secrecy outage probability to prevent uploaded model parameters from being wiretapped by a malicious eavesdropper. Then, we formulate a joint UAV placement and resource allocation problem for minimizing training time and UE energy consumption while maximizing the number of secure UEs under the UAV's energy constraint. Considering the random movement of the eavesdropper and UEs as well as online task generation on UEs in practical application scenarios, we present the long short-term memory (LSTM)-based deep deterministic policy gradient (DDPG) algorithm (LSTM-DDPG) to facilitate real-time decision making for the formulated problem. Finally, simulation results show that the proposed LSTM-DDPG algorithm outperforms the state-of-arts in terms of efficiency and security of FL.
关键词	Training Security Autonomous aerial vehicles Energy consumption Resource management Data models Computational modeling Deep reinforcement learning federated learning (FL) physical layer security resource allocation unmanned aerial vehicle (UAV)
DOI	10.1109/TVT.2023.3347912
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China
WOS研究方向	Engineering ; Telecommunications ; Transportation
WOS类目	Engineering, Electrical & Electronic ; Telecommunications ; Transportation Science & Technology
WOS记录号	WOS:001224392800004
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40029
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Liu, Min
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Tsinghua Univ, Inst Network Sci & Cyberspace, Beijing 100084, Peoples R China 4.Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China 5.Zhongguancun Lab, Beijing 100194, Peoples R China
推荐引用方式 GB/T 7714	Fan, Xiaokun,Chen, Yali,Liu, Min,et al. UAV-Enabled Federated Learning in Dynamic Environments: Efficiency and Security Trade-Off[J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,2024,73(5):6993-7006.
APA	Fan, Xiaokun.,Chen, Yali.,Liu, Min.,Sun, Sheng.,Liu, Zhuotao.,...&Li, Zhongcheng.(2024).UAV-Enabled Federated Learning in Dynamic Environments: Efficiency and Security Trade-Off.IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,73(5),6993-7006.
MLA	Fan, Xiaokun,et al."UAV-Enabled Federated Learning in Dynamic Environments: Efficiency and Security Trade-Off".IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 73.5(2024):6993-7006.