Learning to Detect Malicious Clients for Robust Federated Learning

Understand

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.
• backdoor attacks).
• Therefore, timely detecting these malicious model updates and the underlying attackers becomes critically important.