FedReview: Review and Dispose Poisoned Updates without Validation Datasets or Historic Knowledge
FedReview detects poisoned federated-learning updates without requiring validation datasets or historical baselines, lowering the barrier to Byzantine-robust distributed training.
Summary written by editorial AI · Source link below
arXiv:2402.16934v2 Announce Type: replace-cross Abstract: Federated learning has emerged as a decentralized approach for training high-performance models without accessing user data. Despite its effectiveness, it is vulnerable to poisoning attacks, where malicious users manipulate the global model by uploading poisoned updates. In this paper, we propose FedReview, a review-based mechanism to identify and dispose the potential poisoned updates in federated learning. Under FedReview, the server r
Editorial Analysis
Enterprises adopting federated learning for privacy-preserving model training face poisoning risks that are hard to detect without clean validation data; FedReview removes that dependency.
If running federated learning workloads, evaluate validation-free poisoning detection approaches to close a common gap in distributed model integrity.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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