UAV-Enabled Federated Learning in Dynamic Environments: Efficiency and Security Trade-Off
文献类型：期刊论文
| 作者 | Fan, Xiaokun2,4; Chen, Yali2; Liu, Min2,4; Sun, Sheng2; Liu, Zhuotao3; Xu, Ke1,5; Li, Zhongcheng2,4 |
| 刊名 | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
 |
| 出版日期 | 2024-05-01 |
| 卷号 | 73期号:5页码:6993-7006 |
| 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) |
| ISSN号 | 0018-9545 |
| DOI | 10.1109/TVT.2023.3347912 |
| 英文摘要 | 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. |
| 资助项目 | National Key Research and Development Program of China |
| WOS研究方向 | Engineering ; Telecommunications ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:001224392800004 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/40028]  |
| 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.Zhongguancun Lab, Beijing 100194, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Tsinghua Univ, Inst Network Sci & Cyberspace, Beijing 100084, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, 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. |
入库方式： OAI收割
来源：计算技术研究所
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