论文标题
学习倒置：联合学习中的简单自适应攻击，用于梯度反演
Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning
论文作者
论文摘要
梯度反转攻击使从联合学习（FL）中的模型梯度恢复了训练样本，并对数据隐私构成了严重威胁。为了减轻这种脆弱性，先前的工作提出了基于差异隐私的原则防御，以及基于梯度压缩作为对策的启发式防御。到目前为止，这些防御能力非常有效，特别是基于梯度压缩的防御能力，该防御能够使模型保持高精度，同时大大降低了攻击的有效性。在这项工作中，我们认为这样的发现低估了佛罗里达州的隐私风险。作为反例，我们表明现有的防御能力可以通过简单的自适应攻击破坏，在该攻击中，经过辅助数据训练的模型能够在视觉和语言任务上倾斜梯度。
Gradient inversion attack enables recovery of training samples from model gradients in federated learning (FL), and constitutes a serious threat to data privacy. To mitigate this vulnerability, prior work proposed both principled defenses based on differential privacy, as well as heuristic defenses based on gradient compression as countermeasures. These defenses have so far been very effective, in particular those based on gradient compression that allow the model to maintain high accuracy while greatly reducing the effectiveness of attacks. In this work, we argue that such findings underestimate the privacy risk in FL. As a counterexample, we show that existing defenses can be broken by a simple adaptive attack, where a model trained on auxiliary data is able to invert gradients on both vision and language tasks.
