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数千甚至数百万参与者能够对高维模型进行保护隐私的联合学习。然而,由于这些使用案例的规模,不可能对这些协议进行端到端的经验性评估。我们介绍了OLYMPIA,这是一个通过模拟对安全协议进行经验性评估的框架。OLYMPIA为确定协议提供了嵌入的域名语言,并为评估其绩效提供了一个模拟框架。我们运用OLYMPIA实施了最近几个安全集合协议,并对其运行的端到端时间进行了第一次经验性比较。我们释放OLYMPIA作为开放源。