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Bringing Order to Asynchronous SGD: Towards Optimality under Data-Dependent Delays with Momentum
Asynchronous SGD enables scalable distributed training but suffers from stale gradients. When delays depend on the data, slower samples may be underrepresented, biasing training toward faster samples. We introduce ordered momentum, a unified framework that attains the best-known rates for smooth con
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