Recent secure aggregation protocols enable privacy-preserving federated learning for high-dimensional models among thousands or even millions of participants. Due to the scale of these use cases, however, end-to-end empirical evaluation of these protocols is impossible. We present OLYMPIA, a framework for empirical evaluation of secure protocols via simulation. OLYMPIA. provides an embedded domain-specific language for defining protocols, and a simulation framework for evaluating their performance. We implement several recent secure aggregation protocols using OLYMPIA, and perform the first empirical comparison of their end-to-end running times. We release OLYMPIA as open source.

翻译：最近的安全聚合协议使得在成千上万个甚至数百万个参与者之间进行高维模型的隐私保护联邦学习成为可能。然而，由于这些用例的规模庞大，对这些协议进行端到端的经验评估是不可能的。我们提出了OLYMPIA，这是一个通过仿真对安全协议进行经验评估的框架。OLYMPIA提供了一个嵌入式领域特定语言用于定义协议，以及一个用于评估其性能的仿真框架。我们使用OLYMPIA实现了几种最新的安全聚合协议，并首次对它们的端到端运行时间进行了经验比较。我们将OLYMPIA作为开源软件发布。