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

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.