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AntiFLipper: A Secure and Efficient Defense Against Label-Flipping Attacks in Federated Learning
Federated learning (FL) enables privacy-preserving model training by keeping data decentralized. However, it remains vulnerable to label-flipping attacks, where malicious clients manipulate labels to poison the global model. Despite their simplicity, these attacks can severely degrade model performa
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