An accelerated one-step-delayed stochastic method achieves the centralized O(1/sqrt(N)) rate with M up to sqrt(rho) N^{3/4} workers and a matching lower bound for linear-span first-order methods.
1910.09126 , archivePrefix=
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High-probability generalization bounds for D-SGD are derived at the optimal rate O(1/sqrt(mn) log(1/δ)) via pointwise uniform stability across convex and non-convex settings.
Decentralized SGD and SGDA under Markovian sampling admit non-asymptotic generalization bounds that incorporate network topology, Markov mixing rates, and primal-dual dynamics.
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Near-Optimal Decentralized Stochastic Convex Optimization over Networks
An accelerated one-step-delayed stochastic method achieves the centralized O(1/sqrt(N)) rate with M up to sqrt(rho) N^{3/4} workers and a matching lower bound for linear-span first-order methods.
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Unveiling High-Probability Generalization in Decentralized SGD
High-probability generalization bounds for D-SGD are derived at the optimal rate O(1/sqrt(mn) log(1/δ)) via pointwise uniform stability across convex and non-convex settings.
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Stability and Generalization for Decentralized Markov SGD
Decentralized SGD and SGDA under Markovian sampling admit non-asymptotic generalization bounds that incorporate network topology, Markov mixing rates, and primal-dual dynamics.