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.
Convergence in high probability of distributed stochastic gradient descent algorithms
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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.