Differentially Private Linear Regression over Fully Decentralized Datasets
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This paper presents a differentially private algorithm for linear regression learning in a decentralized fashion. Under this algorithm, privacy budget is theoretically derived, in addition to that the solution error is shown to be bounded by O(t) for O(1/t) descent step size and O((t^1-e)) for O(t^-e) descent step size.
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