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 rates of distributed gradient methods under random quantization: a stochastic approximation approach
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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.