MODEL GRADIENT UPDATE METHOD AND DEVICE
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United States of America
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Issued Date -
Nov 14, 2024
app pub date -
Aug 15, 2022
filing date -
Aug 15, 2022
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Abstract
The present application provides a model gradient update method and device, for use in improving the accuracy of model training. A central server repeatedly executes a gradient update process until a stop condition is satisfied. One gradient update process comprises: receiving first gradients respectively sent by multiple nodes, the first gradients being obtained by each node using sample data to train a model to be trained of the node one or more times; obtaining a second gradient one the basis of the multiple first gradients and the probability of each node in the present gradient update process, the probability of each node in the present gradient update process being determined by an Actor-Critic network one the basis of the probability of each node in the last gradient update process; and sending the second gradient to the multiple nodes, respectively.
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