Mitigating Backdoor Attacks in Federated Learning

Understand

Malicious clients can attack federated learning systems using malicious data, including backdoor samples, during the training phase.

• The compromised global model will perform well on the validation dataset designed for the task, but a small subset of data with backdoor patterns may trigger the model to make a wrong prediction.
• There has been an arms race between attackers who tried to conceal attacks and defenders who tried to detect attacks during the aggregation stage of training on the server-side.
• In this work, we propose a new and effective method to mitigate backdoor attacks after the training phase.