基于无人机辅助联邦边缘学习通信系统的安全隐私能效研究

卢为党; 冯凯; 丁雨; 李博; 赵楠

doi:10.11999/JEIT240847

基于无人机辅助联邦边缘学习通信系统的安全隐私能效研究

doi: 10.11999/JEIT240847

卢为党¹,
冯凯¹,
丁雨^1, ,,
李博²,
赵楠³

1.
浙江工业大学信息工程学院杭州 310023
2.
哈尔滨工业大学(威海)信息科学与工程学院威海 264209
3.
大连理工大学信息与通信工程学院大连 116024

基金项目: 国家自然科学基金(62271447)，浙江省属高校基本科研业务费专项资金(RF-C2023008)

详细信息

作者简介:
卢为党：男，教授，博士生导师，研究方向为智能通信、无人机通信、安全通信等

冯凯：男，硕士生，研究方向为联邦学习等

丁雨：女，博士生，研究方向为无人机安全通信等

李博：男，教授，博士生导师，研究方向为空天地海一体化信息网络、6G移动通信等

赵楠：男，教授，博士生导师，研究方向为无人机通信与网络、安全与隐蔽通信等

通讯作者:
丁雨　2112003309@zjut.edu.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2024-10-09
- 修回日期: 2024-12-20
- 网络出版日期: 2025-01-17

Research on Security, Privacy, and Energy Efficiency in Unmanned Aerial Vehicle-Assisted Federal Edge Learning Communication Systems

LU Weidang¹,
FENG Kai¹,
DING Yu^{1
, ,},
LI Bo²,
ZHAO Nan³

1.
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310014, China
2.
School of Information Science and Engineering, Harbin Institute of Technology, Weihai 264209, China
3.
School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China

Funds: The National Natural Science Foundation of China (62271447), The Fundamental Research Funds for the Provincial Universities of Zhejiang (RF-C2023008)

摘要

摘要: 无人机(UAV)辅助联邦边缘学习的通信能够有效解决终端设备数据孤岛问题和数据泄露风险。然而，窃听者可能利用联邦边缘学习中的模型更新来恢复终端设备的原始隐私数据，从而对系统的隐私安全构成极大威胁。为了克服这一挑战，该文在无人机辅助联邦边缘学习通信系统提出一种有效的安全聚合和资源优化方案。具体来说，终端设备利用其本地数据进行局部模型训练来更新参数，并将其发送给全局无人机，无人机据此聚合出新的全局模型参数。窃听者试图通过窃听终端设备发送的模型参数信号来恢复终端设备的原始数据。该文通过联合优化终端设备的传输带宽、CPU频率、发送功率以及无人机的CPU频率，最大化安全隐私能效。为了解决该优化问题，该文提出一种演进深度确定性策略梯度(DDPG)算法，通过和系统智能交互，在保证基本时延和能耗需求的情况下获得安全聚合和资源优化方案。最后，通过和基准方案对比，验证了所提方案的有效性。
- 无人机 /
- 联邦边缘学习 /
- 能效 /
- 资源优化 /
- 深度确定性策略梯度
Abstract: Objective Unmanned Aerial Vehicle-Assisted Federal Edge Learning (UAV-Assisted FEL) communication addresses the data isolation problem and mitigates data leakage risks in terminal devices. However, eavesdroppers may exploit model updates in FEL to recover original private data, significantly threatening the system’s privacy and security. Methods To address this issue, this study proposes a secure aggregation and resource optimization scheme for UAV-Assisted FEL communication systems. Terminal devices train local models using local data and update parameters, which are transmitted to a global UAV. The UAV aggregates these parameters to generate new global model parameters. Eavesdroppers attempt to intercept the transmitted parameters to reconstruct the original data. To enhance security-privacy energy efficiency, the transmission bandwidth, CPU frequency, and transmit power of terminal devices, along with the CPU frequency of the UAV, are jointly optimized. An evolutionary Deep Deterministic Policy Gradient (DDPG) algorithm is proposed to solve this optimization problem. The algorithm intelligently interacts with the system to achieve secure aggregation and resource optimization while meeting latency and energy consumption requirements. Results and Discussions The simulation results validate the effectiveness of the proposed scheme. The experiments evaluate the effects of the scheme on key performance metrics, including system cost, secure transmission rate, and secure privacy energy efficiency, from multiple perspectives. As shown in (Fig. 2), with an increasing number of terminal devices, system cost, secure transmission rate, and secure privacy energy efficiency all increase. These results indicate that the proposed scheme ensures system security and enhances energy efficiency, even in multi-device scenarios. As shown in (Fig. 3), under varying global iteration counts, the system balances latency and energy consumption by either extending the duration to lower energy consumption or increasing energy consumption to reduce latency. The secure transmission rate rises with the number of global iterations, as fewer iterations allow the system to tolerate higher energy consumption and latency per iteration, leading to reduced transmission power from terminal devices to meet system constraints. Additionally, secure privacy energy efficiency improves with increasing global iterations, further demonstrating the scheme’s capacity to ensure system security and reduce system cost as global iterations increase. As shown in (Fig. 4), during UAV flight, secure privacy energy efficiency fluctuates, with higher secure transmission rates observed when the communication environment between terminal devices and the UAV is more favorable. As shown in (Fig. 5), the proposed scheme is compared with two baseline schemes: Scheme 1, which minimizes system latency, and Scheme 2, which minimizes system energy consumption. The proposed scheme significantly outperforms both baselines in cost overhead. Scheme 1 achieves a slightly higher secure transmission rate than the proposed scheme due to its focus on minimizing latency at the expense of higher energy consumption. Conversely, Scheme 2 shows a considerably lower secure transmission rate as it prioritizes minimizing energy consumption, resulting in lower transmission power and compromised secure transmission rates. The results indicate that the secure privacy energy efficiency of the proposed scheme significantly exceeds that of the baseline schemes, further demonstrating its effectiveness. Conclusions To enhance data transmission security and reduce system costs, this paper proposes a secure aggregation and resource optimization scheme for UAV-Assisted FEL. Under constraints of limited computational and communication resources, the scheme jointly optimizes the transmission bandwidth, CPU frequency, and transmission power of terminal devices, along with the CPU frequency of the UAV, to maximize the secure privacy energy efficiency of the UAV-Assisted FEL system. Given the complexity of the time-varying system and the strong coupling of multiple optimization variables, an advanced DDPG algorithm is developed to solve the optimization problem. The problem is first modeled as a Markov Decision Process, followed by the construction of a reward function positively correlated with the secure privacy energy efficiency objective. The proposed DDPG network then intelligently generates joint optimization variables to obtain the optimal solution for secure privacy energy efficiency. Simulation experiments evaluate the effects of the proposed scheme on key system performance metrics from multiple perspectives. The results demonstrate that the proposed scheme significantly outperforms other benchmark schemes in improving secure privacy energy efficiency, thereby validating its effectiveness.
- Unmanned Aerial Vehicle (UAV) /
- Federated edge learning /
- Energy efficiency /
- Resource optimization /
- Deep Deterministic Policy Gradient (DDPG)

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图 1 无人机辅助联邦边缘学习的通信系统

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图 2 不同终端设备数量下的关键性能指标

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图 3 关键性能指标VS 全局迭代次数

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图 4 不同终端设备数量下的系统最大安全隐私能效

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图 5 不同方案的关键性能指标比较

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表 1 仿真参数设置

参数	值	参数	值	参数	值
$ \left\| {{D_n}} \right\| $	600	$ {p^{\max }}\left( {{\text{dBm}}} \right) $	20	$ {c_0}\left( {{\text{cycles/bit}}} \right) $	1 500
$ \eta $	0.005	$ {f^{\min }}\left( {{\text{MHz}}} \right) $	100	$ {c_n}\left( {{\text{cycles/sample}}} \right) $	$ {\mathcal{U}}\left[ {{{10}^4},3 \times {{10}^4}} \right] $
${\varepsilon _g}$	0.01	$ {f^{\max }}\left( {{\text{GHz}}} \right) $	2	$ BW\left( {{\text{MHz}}} \right) $	3
$ a $	11.95	$ F_U^{\min }\left( {{\text{GHz}}} \right) $	1	$ \psi \left( {{\text{kbits}}} \right) $	21.84
$ b $	0.14	$ F_{\text{U}}^{\max }\left( {{\text{GHz}}} \right) $	6	$ N $	3～9
$ {\beta _0} $	$ 7 \times {10^{ - 5}} $	$ \delta _0^2 = \delta _1^2\left( {{\text{dBm}}} \right) $	-90	$ E_n^{\max }\left( {\text{J}} \right) $	20
$ {p^{\min }}\left( {{\text{dBm}}} \right) $	10	$ \sigma _{n,m}^2\left( {{\text{dBm}}} \right) $	-70	$ E_U^{\max }\left( {\text{J}} \right) $	30

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