保护隐私的联邦学习框架，实现加密有损图像重建
PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction
- 用联邦学习+差分隐私+水印技术保护本地数据
- 性能接近中心化方法，计算开销更低
- 适合医疗、安防等敏感图像协作场景
使用残差密集空间网络（RDSNs）从低分辨率输入重建高质量图像至关重要但极具挑战性。在多方协作的集中式训练中，数据泄露和推理攻击风险更高，且计算与通信成本巨大。本文提出一种面向加密有损图像重建的隐私保护联邦学习框架（PPFL-RDSN），融合联邦学习（FL）、局部差分隐私与鲁棒模型水印技术，确保数据始终保留在本地客户端，保护隐私信息，并验证模型真实性而不暴露原始数据。实验表明，PPFL-RDSN在性能上可媲美当前最优的集中式方法，同时显著降低计算负担，有效缓解安全与隐私漏洞，为安全可靠的协作式计算机视觉应用提供实用解决方案。
原文摘要 · Abstract (English)
Reconstructing high-quality images from low-resolution inputs using Residual Dense Spatial Networks (RDSNs) is crucial yet challenging. It is even more challenging in centralized training where multiple collaborating parties are involved, as it poses significant privacy risks, including data leakage and inference attacks, as well as high computational and communication costs. We propose a novel Privacy-Preserving Federated Learning-based RDSN (PPFL-RDSN) framework specifically tailored for encrypted lossy image reconstruction. PPFL-RDSN integrates Federated Learning (FL), local differential privacy, and robust model watermarking techniques to ensure that data remains secure on local clients/devices, safeguards privacy-sensitive information, and maintains model authenticity without revealing underlying data. Empirical evaluations show that PPFL-RDSN achieves comparable performance to the state-of-the-art centralized methods while reducing computational burdens, and effectively mitigates security and privacy vulnerabilities, making it a practical solution for secure and privacy-preserving collaborative computer vision applications.
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