Active Membership Inference Attack under Local Differential Privacy in Federated Learning. (arXiv:2302.12685v1 [cs.LG])

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 …

ami attack attacks clients data data privacy differential privacy effectively federated learning framework global local malicious privacy protection server target under