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arXiv AI By Tianhang Zheng, Yanlu Li, Bohan Deng, Baochun Li

FedReview: Review and Dispose Poisoned Updates without Validation Datasets or Historic Knowledge

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FedReview is a review-based mechanism for federated learning that identifies and removes poisoned model updates without needing a server-side validation dataset or historic client knowledge. In each training round, the server randomly selects reviewers who evaluate incoming updates on their own training data, rank them, and estimate how many low-quality updates are likely poisoned. The server then aggregates these rankings via majority voting to filter out suspicious updates during model aggregation, enabling robust global model training even in adversarial settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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