Abstract:Differentially private distributed stochastic optimization has become a hot topic due to the urgent need of privacy protection in distributed stochastic optimization. In this paper, two-time scale stochastic approximation-type algorithms for differentially private distributed stochastic optimization with time-varying sample sizes are proposed using gradient-and output-perturbation methods. For both gradient-and output-perturbation cases, the convergence of the algorithm and differential privacy with a finite c… Show more
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