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Convergence Analysis of Noisy Distributed Gradient Descent for Non-convex Optimization -- Saddle Point Escape
This paper studies noisy distributed gradient descent (\textbf{NDGD}) for smooth non-convex finite-sum optimization over networks. Random perturbations enable saddle-point escape while preserving distributed implementation and consensus. Under suitable regularity conditions, \textbf{NDGD} converges
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