Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications. (arXiv:2207.08486v2 [cs.LG] UPDATED)

Adversarial attacks such as poisoning attacks have attracted the attention of
many machine learning researchers. Traditionally, poisoning attacks attempt to
inject adversarial training data in order to manipulate the trained model. In
federated learning (FL), data poisoning attacks can be generalized to model
poisoning attacks, which cannot be detected by simpler methods due to the lack
of access to local training data by the detector. State-of-the-art poisoning
attack detection methods for FL have various weaknesses, e.g., the number of
attackers …

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