Federated learning is a privacy-by-design framework that enables training deep neural networks from decentralized sources of data, but it is fraught with innumerable attack surfaces. We provide a taxonomy of recent attacks on federated learning systems and detail the need for more robust threat modeling in federated learning environments.

中文翻译：

---

联合学习攻击的分类法

联合学习是一种按设计保护隐私的框架，可以从分散的数据源中训练深度神经网络，但是它充满了无数的攻击面。我们提供了对联合学习系统近期攻击的分类，并详细说明了在联合学习环境中需要更强大的威胁建模的需求。