提出最优防御方法，在保护数据隐私的同时提升模型性能。
Optimal Defenses Against Gradient Reconstruction Attacks
- 基于理论下界定制噪声与剪枝策略，实现防御优化
- 在保持模型性能前提下，显著降低梯度重构攻击成功率
- 适合关注联邦学习隐私安全的研究者与开发者
联邦学习通过分布式训练避免数据集中存储，但仍面临梯度重构攻击风险。本文推导了添加噪声和梯度剪枝两种标准防御方法的重构误差理论下界，并据此设计参数与模型自适应的定制化防御机制，以实现隐私保护与模型效用之间的最优权衡。实验表明，所提方法在更有效防护训练数据泄露的同时，相比梯度噪声和梯度剪枝方法，仍能获得更高的模型性能。
原文摘要 · Abstract (English)
Federated Learning (FL) is designed to prevent data leakage through collaborative model training without centralized data storage. However, it remains vulnerable to gradient reconstruction attacks that recover original training data from shared gradients. To optimize the trade-off between data leakage and utility loss, we first derive a theoretical lower bound of reconstruction error (among all attackers) for the two standard methods: adding noise, and gradient pruning. We then customize these two defenses to be parameter- and model-specific and achieve the optimal trade-off between our obtained reconstruction lower bound and model utility. Experimental results validate that our methods outperform Gradient Noise and Gradient Pruning by protecting the training data better while also achieving better utility.
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