Diffusion strategy for distributed learning escapes saddle points in O(1/μ) iterations and returns approximate second-order stationary points in polynomial iterations with less restrictive noise assumptions than centralized methods.
SPIDER: Near-opt imal non- convex optimization via stochastic path-integrated differential estimator
2 Pith papers cite this work. Polarity classification is still indexing.
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Diffusion learning achieves linear-rate agreement around the network centroid in stochastic non-convex distributed optimization.
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Distributed Learning in Non-Convex Environments -- Part II: Polynomial Escape from Saddle-Points
Diffusion strategy for distributed learning escapes saddle points in O(1/μ) iterations and returns approximate second-order stationary points in polynomial iterations with less restrictive noise assumptions than centralized methods.
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Distributed Learning in Non-Convex Environments -- Part I: Agreement at a Linear Rate
Diffusion learning achieves linear-rate agreement around the network centroid in stochastic non-convex distributed optimization.