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.

翻译：利用残差密集空间网络（RDSNs）从低分辨率输入中重建高质量图像至关重要且具有挑战性。在涉及多方协作的集中式训练中，这一问题尤为困难，因为它带来了显著的数据泄露和推理攻击等隐私风险，以及高昂的计算和通信成本。我们提出了一种新颖的基于隐私保护联邦学习的残差密集空间网络（PPFL-RDSN）框架，专门针对加密有损图像重建任务而设计。PPFL-RDSN融合了联邦学习（FL）、本地差分隐私和鲁棒模型水印技术，确保数据在本地客户端/设备上保持安全，保护隐私敏感信息，并在不暴露底层数据的情况下维持模型真实性。实证评估表明，PPFL-RDSN在降低计算负担的同时，达到了与最先进的集中式方法相当的性能，并有效缓解了安全和隐私漏洞，为安全且隐私保护的协作计算机视觉应用提供了一种实用解决方案。