论文标题
使用差分测试和离群检测的联邦学习中的后门防御
Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection
论文作者
论文摘要
联合学习（FL）的目标是通过在不访问用户的私人数据的情况下独立更新在边缘设备上更新的模型参数来训练一个全局模型。但是，FL容易受到后门攻击的影响，其中一小部分恶意代理通过将污染的模型更新上传到服务器中，将目标错误分类行为注入了目标错误分类行为。在这项工作中，我们提出了Diffense，这是一种自动防御框架，可通过利用差分测试和两步性疯狂的异常检测来保护FL系统免受后门攻击，而无需任何先前的攻击方案知识或直接访问本地模型参数。我们从经验上表明，我们的检测方法可以防止各种潜在的攻击者，同时始终如一地实现与联邦平均训练的全球模型的收敛性（FedAvg）。我们进一步证实了方法对先前的防御技术的有效性和概括性，例如多krum和坐标的中位数聚集。我们的检测方法将全局模型的平均后门准确度降低到4％以下，并达到零的假负率。
The goal of federated learning (FL) is to train one global model by aggregating model parameters updated independently on edge devices without accessing users' private data. However, FL is susceptible to backdoor attacks where a small fraction of malicious agents inject a targeted misclassification behavior in the global model by uploading polluted model updates to the server. In this work, we propose DifFense, an automated defense framework to protect an FL system from backdoor attacks by leveraging differential testing and two-step MAD outlier detection, without requiring any previous knowledge of attack scenarios or direct access to local model parameters. We empirically show that our detection method prevents a various number of potential attackers while consistently achieving the convergence of the global model comparable to that trained under federated averaging (FedAvg). We further corroborate the effectiveness and generalizability of our method against prior defense techniques, such as Multi-Krum and coordinate-wise median aggregation. Our detection method reduces the average backdoor accuracy of the global model to below 4% and achieves a false negative rate of zero.
