研究信息和自动化科学，促进科技发展。信息与通信工程研究 控制科学与工程研究 计算机科学与技术研究 信息系统集成及设备装配研究 软件设计开发 相关学历教育、继续教育、专业培训、学术交流与博士后培养 《自动化学报》出版
Federated learning was developed to solve the data privacy and data island in traditional machine learning. Existing federated learning methods use multiple participants who do not share private data to jointly train a better global model. However, research shows that security problems in federated learning remain numerous. Typically, federated learning is attacked by malicious participants during training, resulting in the failure of the global model and the leakage of the private data of the participants. This paper studies the effectiveness of adversarial example poisoning attacks on federated learning and further finds potential security problems in federated learning. Although adversarial examples are often used to attack machine learning models during testing, in this paper, malicious participants use adversarial examples for training the local models, aiming to make the local model learn chaotic sample classification features, thereby generating malicious local model parameters.To let the malicious participants dominate the federal learning and training process, we further use a strategy of “learning rate amplification.” Experiments show that compared with the Fed-Deepconfuse attack method, the attacks in this paper achieve better attack performance on the CIFAR10 and MNIST datasets.
