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

arXiv:2402.16934v1 Announce Type: cross
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 …

access arxiv cs.ai cs.cr cs.lg data decentralized decentralized approach federated federated learning high learn malicious mechanism opportunities performance review server updates user data