Accelerated Decentralized Stochastic Gradient Descent for Strongly Convex Optimization
Decentralized stochastic optimization is a fundamental paradigm for large-scale learning over networks, where agents communicate only with their neighbors and no central coordinator is required. For strongly convex problems, communication efficiency is mainly determined by the condition number \(κ=L/μ\) and the network spectral gap \(1-β\). Although deterministic decentralized methods can simultaneously achieve accelerated \(\sqrtκ\) and \(1/\sqrt{1-β}\) dependences, no existing stochastic method attains both improvements at once. In this paper, we propose \emph{Multi-Gossip Accelerated DSGD} (MG-ADSGD), a decentralized stochastic algorithm that combines Nesterov-type primal--dual extrapo...
| Authors | Ming Sun, Kun Yuan |
|---|---|
| Primary category | cs.LG |
| Published | 5 June 2026 |
| Last updated at source | 5 June 2026 |
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