FedReview: A Review Mechanism for Rejecting Poisoned Updates in Federated Learning

arXiv:2402.16934v1 Announce Type: new
Abstract: Federated learning has recently emerged as a decentralized approach to learn a high-performance model without access to user data. Despite its effectiveness, federated learning gives malicious users opportunities to manipulate the model by uploading poisoned model updates to the server. In this paper, we propose a review mechanism called FedReview to identify and decline the potential poisoned updates in federated learning. Under our mechanism, the server randomly assigns a subset of clients as reviewers to …

abstract arxiv cs.ai cs.cr cs.lg data decentralized federated learning learn opportunities paper performance review server type updates user data