Federated learning (FL) was originally regarded as a framework for collaborative learning among clients with data privacy protection through a coordinating server. In this paper, we propose a new active membership inference (AMI) attack carried out by a dishonest server in FL. In AMI attacks, the server crafts and embeds malicious parameters into global models to effectively infer whether a target data sample is included in a client's private training data or not. By exploiting the correlation among data features through a non-linear decision boundary, AMI attacks with a certified guarantee of success can achieve severely high success rates under rigorous local differential privacy (LDP) protection; thereby exposing clients' training data to significant privacy risk. Theoretical and experimental results on several benchmark datasets show that adding sufficient privacy-preserving noise to prevent our attack would significantly damage FL's model utility.

翻译：联邦学习（FL）最初被认为是一种通过协调服务器实现客户端间具有数据隐私保护的协作学习框架。本文提出了一种由FL中不诚实服务器实施的新型主动成员推理（AMI）攻击。在AMI攻击中，服务器精心制作并将恶意参数嵌入全局模型，以有效推断目标数据样本是否包含在客户端的私有训练数据中。通过利用非线性决策边界下数据特征之间的相关性，具备成功认证保证的AMI攻击能够在严格的本地差分隐私（LDP）保护下达到极高的成功率，从而使客户端的训练数据面临显著的隐私风险。在多个基准数据集上的理论和实验结果表明，为防止此类攻击而添加足够强的隐私保护噪声将严重损害FL的模型效用。