Recovering Labels from Local Updates in Federated Learning

arXiv:2405.00955v1 Announce Type: cross
Abstract: Gradient inversion (GI) attacks present a threat to the privacy of clients in federated learning (FL) by aiming to enable reconstruction of the clients' data from communicated model updates. A number of such techniques attempts to accelerate data recovery by first reconstructing labels of the samples used in local training. However, existing label extraction methods make strong assumptions that typically do not hold in realistic FL settings. In this paper we present a novel label …

accelerate arxiv attacks clients cs.cr cs.lg data data recovery enable federated federated learning local privacy recovery techniques threat updates