Federated learning (FL) has rapidly risen in popularity due to its promise of privacy and efficiency. Previous works have exposed privacy vulnerabilities in the FL pipeline by recovering user data from gradient updates. However, existing attacks fail to address realistic settings because they either 1) require a `toy' settings with very small batch sizes, or 2) require unrealistic and conspicuous architecture modifications. We introduce a new strategy that dramatically elevates existing attacks to operate on batches of arbitrarily large size, and without architectural modifications. Our model-agnostic strategy only requires modifications to the model parameters sent to the user, which is a realistic threat model in many scenarios. We demonstrate the strategy in challenging large-scale settings, obtaining high-fidelity data extraction in both cross-device and cross-silo federated learning.

翻译：联邦学习(FL)由于其对隐私和效率的许诺而迅速增加。以前的工程通过从梯度更新中恢复用户数据,暴露了FL管道中的隐私弱点。然而,现有的攻击未能解决现实环境,因为它们:(1) 需要“玩具”环境,其批量大小很小,或(2) 需要不现实和明显的建筑改造。我们引入了新战略,大幅提升现有攻击,对任意大体积和不进行建筑改造的批量进行操作。我们的模型认知战略只要求修改发给用户的模型参数,在许多情景中,这是一个现实的威胁模型。我们展示了挑战大规模环境的战略,在跨构件和跨轴联学习中获取高度虚构的数据提取。