On Privacy-Preserving Image Transmission in Low-Altitude Networks: A Swin Transformer-Based Framework with Federated Learning

2026-05-12

AI Summary

This work addresses the dual challenges of limited bandwidth and privacy preservation in low-altitude unmanned aerial vehicle (UAV) image transmission. The authors propose a novel framework that integrates semantic communication with federated learning, deploying dedicated nodes on UAVs to extract multi-scale semantic features using Swin Transformers. By leveraging federated learning, the system enables distributed model training without sharing raw data, thereby enhancing privacy. This approach represents the first integration of Swin Transformers and federated learning for semantic communication in low-altitude imagery. Evaluated on CIFAR-10, the method achieves at least a 5.7 dB improvement in peak signal-to-noise ratio (PSNR) over the DeepJSCC baseline, significantly boosting transmission efficiency under bandwidth constraints while improving model convergence and generalization capabilities.

Abstract

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.

Problem

• privacy-preserving
• image transmission
• low-altitude networks
• bandwidth constraint
• UAV

Innovation

• Swin Transformer
• Semantic Communication
• Federated Learning
• UAV Networks
• Privacy-Preserving Transmission