The Privacy Power of Correlated Noise in Decentralized Learning

arXiv:2405.01031v1 Announce Type: new
Abstract: Decentralized learning is appealing as it enables the scalable usage of large amounts of distributed data and resources (without resorting to any central entity), while promoting privacy since every user minimizes the direct exposure of their data. Yet, without additional precautions, curious users can still leverage models obtained from their peers to violate privacy. In this paper, we propose Decor, a variant of decentralized SGD with differential privacy (DP) guarantees. Essentially, in Decor, users securely …

abstract arxiv cs.cr cs.dc cs.lg data decentralized distributed distributed data every math.oc noise power privacy resources scalable stat.ml type usage while