Enhancing Privacy in Federated Learning via Dual Obfuscation of Gradients and Training Images

2026-08-20

AI Summary

本文提出一种双重混淆方法，通过同时混淆梯度信息和训练图像来增强联邦学习中的隐私保护，减少数据恢复攻击的风险。

Abstract

Federated learning enables collaborative model training while keeping data locally at each client; however, recent studies have shown that training data can be reconstructed from shared model updates. To address this issue, this paper proposes a dual obfuscation method that enhances robustness against image restoration attacks by jointly obfuscating updated information and training images. The proposed method combines a robustness enhancement technique based on random binary weights, which randomly sets a portion of gradient elements to zero, with an image encryption technique. These techniques provide complementary protection by reducing the amount of original gradient information available to an attacker and the visual interpretability of reconstructed images, respectively. Furthermore, the image encryption technique allows independent keys to be used for each client and each image, avoiding explicit key sharing. Experimental results on an image classification task using a Vision Transformer (ViT) show that the proposed method reduces the visual information recovered by Attention Privacy Leakage (APRIL) under the evaluated settings without causing additional degradation in classification performance beyond that caused by image encryption. Although the proposed combination does not provide an absolute security guarantee, the results demonstrate the potential benefit of combining gradient modification and image encryption for privacy-enhanced federated learning.

Problem

• Federated Learning
• Privacy
• Data Reconstruction
• Gradient Obfuscation
• Image Encryption

Innovation

• Dual Obfuscation
• Random Binary Weights
• Image Encryption
• Federated Learning