Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks

arXiv:2403.03149v1 Announce Type: new
Abstract: Recent studies have revealed that federated learning (FL), once considered secure due to clients not sharing their private data with the server, is vulnerable to attacks such as client-side training data distribution inference, where a malicious client can recreate the victim's data. While various countermeasures exist, they are not practical, often assuming server access to some training data or knowledge of label distribution before the attack.
In this work, we bridge the gap by proposing …

arxiv attacks can client clients client-side countermeasures cs.cr cs.dc cs.lg data distribution federated federated learning malicious private private data server sharing studies training training data victim vulnerable