论文标题
学会发现恶意客户以进行强大的联盟学习
Learning to Detect Malicious Clients for Robust Federated Learning
论文作者
论文摘要
联合学习系统容易受到恶意客户的攻击。由于系统中的中央服务器无法管理客户端的行为，因此Rogue客户端可以通过向服务器发送恶意模型更新来启动攻击，从而降低学习性能或执行目标模型中毒攻击（又称Backdoor攻击）。因此，及时检测这些恶意模型的更新和基础攻击者变得至关重要。在这项工作中，我们提出了一个新的框架，用于强大的联合学习，中央服务器学会使用强大的检测模型来检测和删除恶意模型更新，从而导致针对性的防御。我们通过各种机器学习模型在图像分类和情感分析任务中评估解决方案。实验结果表明，我们的解决方案可确保强大的联邦学习，这对拜占庭式攻击和目标模型中毒攻击都具有韧性。
Federated learning systems are vulnerable to attacks from malicious clients. As the central server in the system cannot govern the behaviors of the clients, a rogue client may initiate an attack by sending malicious model updates to the server, so as to degrade the learning performance or enforce targeted model poisoning attacks (a.k.a. backdoor attacks). Therefore, timely detecting these malicious model updates and the underlying attackers becomes critically important. In this work, we propose a new framework for robust federated learning where the central server learns to detect and remove the malicious model updates using a powerful detection model, leading to targeted defense. We evaluate our solution in both image classification and sentiment analysis tasks with a variety of machine learning models. Experimental results show that our solution ensures robust federated learning that is resilient to both the Byzantine attacks and the targeted model poisoning attacks.
