OLYMPIA: A Simulation Framework for Evaluating the Concrete Scalability of Secure Aggregation Protocols
February 20, 2023 Β· Declared Dead Β· π 2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)
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Authors
Ivoline C. Ngong, Nicholas Gibson, Joseph P. Near
arXiv ID
2302.10084
Category
cs.CR: Cryptography & Security
Citations
2
Venue
2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)
Last Checked
4 months ago
Abstract
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.
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