Rescaled ASGD recovers convergence to the true global objective by rescaling worker stepsizes proportional to computation times, matching the known time lower bound in the leading term under non-convex smoothness and bounded heterogeneity.
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3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
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SUDA-Muon modularizes decentralized Muon via the SUDA template, proving a topology-separated convergence rate of O((1+σ/√N)K^{-1/4}) in nuclear-norm geometry while establishing that tracking-before-polarization is required to avoid non-stationary fixed points and that local-polarize-then-average is
Derives finite-round upper-tail guarantee on population-empirical gap for client-sampled orthogonalized matrix momentum under heterogeneous data, with Lipschitz condition on the orthogonalizer.
citing papers explorer
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Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity
Rescaled ASGD recovers convergence to the true global objective by rescaling worker stepsizes proportional to computation times, matching the known time lower bound in the leading term under non-convex smoothness and bounded heterogeneity.
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SUDA-Muon: Structural Design Principles and Boundaries for Fully Decentralized Muon
SUDA-Muon modularizes decentralized Muon via the SUDA template, proving a topology-separated convergence rate of O((1+σ/√N)K^{-1/4}) in nuclear-norm geometry while establishing that tracking-before-polarization is required to avoid non-stationary fixed points and that local-polarize-then-average is
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A Note on Stability for Orthogonalized Matrix Momentum with Client Sampling
Derives finite-round upper-tail guarantee on population-empirical gap for client-sampled orthogonalized matrix momentum under heterogeneous data, with Lipschitz condition on the orthogonalizer.