Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning. (arXiv:2306.03013v2 [cs.CR] UPDATED)

Malicious server (MS) attacks have enabled the scaling of data stealing in
federated learning to large batch sizes and secure aggregation, settings
previously considered private. However, many concerns regarding client-side
detectability of MS attacks were raised, questioning their practicality once
they are publicly known. In this work, for the first time, we thoroughly study
the problem of client-side detectability.We demonstrate that most prior MS
attacks, which fundamentally rely on one of two key principles, are detectable
by principled client-side checks. …

aggregation attacks batch client client-side data data stealing federated learning large malicious private scaling server settings stealing