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Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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inferred← citeshttps://doi.org/10.18280/isi.270117openalex-snapshot
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inferred← citeshttps://doi.org/10.2196/43664openalex-snapshot
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inferred← citeshttps://doi.org/10.2196/52171openalex-snapshot
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inferred← citeshttps://doi.org/10.2196/60003openalex-snapshot
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inferred← citeshttps://doi.org/10.32604/cmes.2023.027226openalex-snapshot
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