Learning to Detect Malicious Clients for Robust Federated Learning

1 Feb 2020Suyi LiYong ChengWei WangYang LiuTianjian Chen

Federated learning systems are vulnerable to attacks from malicious clients. As the central server in the system cannot govern the behaviors of the clients, a rogue client may initiate an attack by sending malicious model updates to the server, so as to degrade the learning performance or enforce targeted model poisoning attacks (a.k.a... (read more)

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