An adaptive federated-learning backdoor attack uses membership-inference feedback on the global model to keep malicious updates statistically similar to benign ones, evading nine robust aggregation defenses in two image datasets.
Deep Model Poisoning Attack on Federated Learning,
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Stealth by Conformity: Evading Robust Aggregation through Adaptive Poisoning
An adaptive federated-learning backdoor attack uses membership-inference feedback on the global model to keep malicious updates statistically similar to benign ones, evading nine robust aggregation defenses in two image datasets.