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  1. ; ; (, IEEE Conference on Secure and Trustworthy Machine Learning)
    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 open-source. 
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