A naive gradient step-up function with critical-phase detection can, for selected hyperparameters, make large-batch training match or beat small-batch training accuracy at equal iteration counts, though the proposed teacher-model version is not evaluated.
Communication-Efficient Learning of Deep Net- works from Decentralized Data
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On Using Large-Batches in Federated Learning
A naive gradient step-up function with critical-phase detection can, for selected hyperparameters, make large-batch training match or beat small-batch training accuracy at equal iteration counts, though the proposed teacher-model version is not evaluated.