RFLDefense: robust federated learning defense against targeted attacks

Summary

Researchers propose RFLDefense, a defense against backdoor attacks in federated learning, where attackers inject malicious updates to make the global model fail on specific tasks or inputs. The method combines hybrid similarity calculation, sign consistency verification and robust layer aggregation to suppress malicious updates without relying on external validation datasets. Experiments on three widely used datasets show it defends against various backdoor attacks, outperforming existing defense algorithms with minimal impact on model utility.

Topics

• Adversarial machine learning

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