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