Variance-Reduced Gradient Estimator for Efficient Distributed Optimization of Nonconvex Functions

The authors propose a novel variance-reduced gradient estimator that combines the advantages of 2-point and 2d-point gradient estimators to address the trade-off between convergence rate and sampling cost in distributed zeroth-order optimization for smooth nonconvex functions.