Mitigating Backdoor Attacks in Federated Learning
This addresses security vulnerabilities in federated learning systems for applications like distributed machine learning, offering a post-training mitigation method that is incremental but effective.
The paper tackles the problem of backdoor attacks in federated learning by proposing a federated pruning method to remove redundant neurons and adjust extreme weights, reducing the average attack success rate from 99.7% to 1.9% with a 5.5% loss in test accuracy on Fashion-MNIST, and achieving over 70% reduction with less than 2% accuracy loss on CIFAR-10.
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. Specifically, we design a federated pruning method to remove redundant neurons in the network and then adjust the model's extreme weight values. Our experiments conducted on distributed Fashion-MNIST show that our method can reduce the average attack success rate from 99.7% to 1.9% with a 5.5% loss of test accuracy on the validation dataset. To minimize the pruning influence on test accuracy, we can fine-tune after pruning, and the attack success rate drops to 6.4%, with only a 1.7% loss of test accuracy. Further experiments under Distributed Backdoor Attacks on CIFAR-10 also show promising results that the average attack success rate drops more than 70% with less than 2% loss of test accuracy on the validation dataset.