查看文章

In Internet of Things (IoT) applications, federated learning is commonly used for distributedly training models in a privacy-preserving manner. Recently, federated learning is broadly applied to autonomous driving for training intelligent decision models without disseminating local data remotely. Although federated learning provides a safer training manner for protecting data privacy in autonomous driving, the model training process is still vulnerable to poisoning attacks from vehicle client ends. It is beneficial to study poisoning attacks for enhancing the robustness of the training process to generate reliable decisions for safe driving. Until now, a few researches on poisoning attacks against classification models under federated learning scenarios have been proposed. However, those poisoning attacks against classification tasks cannot be directly applied to regression tasks in a federated learning framework …