FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective Jingwei Sun 1, Ang Li1, Louis DiValentin

Federated learning (FL) is a popular distributed learning framework that trains a global model through iterative communications between a central server and edge devices. Recent works have demonstrated that FL is vulnerable to model poisoning attacks.