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- W3079856735 abstract "We consider information-theoretic privacy in federated submodel learning, where a global server has multiple submodels. Compared to the privacy considered in the conventional federated submodel learning where secure aggregation is adopted for ensuring privacy, information-theoretic privacy provides the stronger protection on submodel selection by the local machine. We propose an achievable scheme that partially adopts the conventional private information retrieval (PIR) scheme that achieves the minimum amount of download. With respect to computation and communication overhead, we compare the achievable scheme with a naïve approach for federated submodel learning with information-theoretic privacy." @default.
- W3079856735 created "2020-08-24" @default.
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- W3079856735 date "2023-06-01" @default.
- W3079856735 modified "2023-09-26" @default.
- W3079856735 title "Information-theoretic privacy in federated submodel learning" @default.
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- W3079856735 doi "https://doi.org/10.1016/j.icte.2022.02.008" @default.
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