Decentralized Cubic Newton method for convex optimization that matches exact centralized iteration complexity with polylogarithmic extra communication rounds under gradient L1-smoothness and Hessian L2-Lipschitz continuity.
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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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Decentralized Inexact Cubic Newton Method with Consensus Procedure
Decentralized Cubic Newton method for convex optimization that matches exact centralized iteration complexity with polylogarithmic extra communication rounds under gradient L1-smoothness and Hessian L2-Lipschitz continuity.
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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.