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Privacy-Preserving Push-Pull Method for Decentralized Optimization via State Decomposition
Distributed optimization is manifesting great potential in multiple fields, e. g., machine learning, control, and resource allocation.
Distributed Online Private Learning of Convex Nondecomposable Objectives
For such problems, we first design a novel generic algorithm framework, named as DPSDA, of differentially private distributed online learning using the Laplace mechanism and the stochastic variants of dual averaging method.
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