RAFT: Evaluating Federated Learning Resilience Against Threats
Mayank Kumar, Radha Agrawal, Priyanka Singh · 2023
Federated Learning (FL) allows for training machine learning models on decentralized data. However, FL has been prone to adversarial attacks. This paper examines the vulnerability of FL towards white-box attacks using the CIFAR10 dataset. In the study, we have used ResNet20 and DenseNet. In the study, the required perturbation is added to find the adversarial samples to fool the model. This decentralized approach to training can make it more difficult for attackers to access the training data, but it can also introduce new vulnerabilities that attackers can exploit. We conducted three types of white box attacks, i.e., Fast Gradient Sign Method (FGSM), Carlini-Wagner (CW), and DeepFool, and studied the model's behavior. We have presented the results of the model behavior considering the different scenarios.