A differentially private gradient-tracking algorithm for distributed stochastic optimization on directed graphs uses subsampling schemes to achieve convergence for nonconvex objectives with finite privacy budget.
Asymptotic properties of S-AB method with diminishing step-size
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Differentially Private Gradient-Tracking-Based Distributed Stochastic Optimization over Directed Graphs
A differentially private gradient-tracking algorithm for distributed stochastic optimization on directed graphs uses subsampling schemes to achieve convergence for nonconvex objectives with finite privacy budget.