用Transformer和联邦学习实现低空无人机图像隐私传输。
On Privacy-Preserving Image Transmission in Low-Altitude Networks: A Swin Transformer-Based Framework with Federated Learning
- 基于Swin Transformer的语义通信框架，提取多尺度语义特征。
- 相比基线提升5.7 dB PSNR，且模型收敛与泛化性能更优。
- 适合低带宽、高隐私需求的无人机图像传输场景。
低空经济的快速发展推动了无人机在物流、巡检和应急响应等领域的广泛应用。然而，无人机向地面站传输高体积图像数据面临带宽受限与隐私要求严苛的双重挑战。为此，提出一种基于联邦学习（FL）的语义通信（SC）框架，实现高效且隐私保护的图像传输。设计了基于Swin Transformer的语义通信（STSC）架构，在带宽受限条件下提取多尺度语义特征。在无人机上部署专用通信与计算节点，提升实时覆盖与灵活性。同时，采用联邦学习机制在分布式设备间训练全局模型，无需共享原始数据，保障用户隐私。在CIFAR-10数据集上的仿真实验表明，所提STSC框架相较DeepJSCC基线在峰值信噪比（PSNR）上至少提升5.7 dB，同时具备更优的收敛性与泛化性能。该框架有效融合了无人机辅助部署、语义通信与隐私保护，为低空网络中的带宽受限图像传输提供了实用解决方案。
原文摘要 · Abstract (English)
The rapid development of low-altitude economy has driven the proliferation of Unmanned Aerial Vehicle (UAV) applications, including logistics, inspection, and emergency response. However, transmitting high-volume image data from UAVs to ground stations faces significant challenges due to limited bandwidth and stringent privacy requirements. To address these issues, a Semantic Communication (SC) framework based on Federated Learning (FL) is proposed for efficient and privacy-preserving image transmission. A Swin Transformer-based Semantic Communication (STSC) architecture is designed to extract multi-scale semantic features under constrained bandwidth conditions. Dedicated communication and computing nodes are deployed on UAVs to enhance real-time coverage and flexibility. Meanwhile, a FL mechanism enables global model training across distributed devices without sharing raw data, thus preserving user privacy. Simulation experiments conducted on the CIFAR-10 dataset demonstrate that the proposed STSC framework achieves at least 5.7 dB improvement in Peak Signal-to-Noise Ratio (PSNR) compared to DeepJSCC baselines, while also showing superior convergence and generalization performance. The framework effectively integrates UAV-assisted deployment with SC and privacy protection, offering a practical solution for bandwidth-constrained image transmission in low-altitude networks.
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