Hammer and Anvil framework categorizes backdoors by update deviation δ and shows that principled combinations of Type-1 outlier/robust and Type-2 removal defenses resist full-information adaptive adversaries.
Mitigating Backdoor Attacks in Federated Learning
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
DMBA maintains attack success rates above 80% for all backdoors in a distributed multi-target FL setting where baselines drop below 50%.
A three-stage pill-based augmentation makes existing FL poisoning attacks evade popular defenses while raising error rates up to 7x on both IID and non-IID data.
citing papers explorer
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Hammer and Anvil: Toward a Theory of Backdoors in Federated Learning
Hammer and Anvil framework categorizes backdoors by update deviation δ and shows that principled combinations of Type-1 outlier/robust and Type-2 removal defenses resist full-information adaptive adversaries.
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Act in Collusion: Distributed Multi-Target Backdoor Attacks in Federated Learning
DMBA maintains attack success rates above 80% for all backdoors in a distributed multi-target FL setting where baselines drop below 50%.
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Poisoning with A Pill: Circumventing Detection in Federated Learning
A three-stage pill-based augmentation makes existing FL poisoning attacks evade popular defenses while raising error rates up to 7x on both IID and non-IID data.