FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients. (arXiv:2207.09209v2 [cs.CR] UPDATED)

Federated learning (FL) is vulnerable to model poisoning attacks, in which
malicious clients corrupt the global model via sending manipulated model
updates to the server. Existing defenses mainly rely on Byzantine-robust FL
methods, which aim to learn an accurate global model even if some clients are
malicious. However, they can only resist a small number of malicious clients in
practice. It is still an open challenge how to defend against model poisoning
attacks with a large number of malicious clients. …

attacks clients federated learning malicious poisoning