Managing training of a machine learning model
AssigneeTelefonaktiebolaget LM Ericsson (publ)
InventorsSelim Ickin, Hassam Riaz, Hannes Larsson
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Abstract
There is provided a method performed by a master node for managing training of a machine learning model. One or more worker nodes of a plurality of worker nodes are selected to train a machine learning model in a round of training. The one or more worker nodes are selected to optimize a performance of an updated machine learning model for a validation dataset after the round of training. The updated machine learning model has one or more parameters of the machine learning model trained by the one or more worker nodes in a previous round of training.
Claims21
A method performed by a master node for managing training of a machine learning model, the method comprising:
- selecting one or more worker nodes of a plurality of worker nodes to train a machine learning model in a round of training,
- wherein the one or more worker nodes are selected to optimize a performance of an updated machine learning model for a validation dataset after the round of training, wherein the updated machine learning model has one or more parameters of the machine learning model trained by the one or more worker nodes in a previous round of training.
20-32. (canceled)
A master node for managing training of a machine learning model, the master node comprising processing circuitry configured to cause the master node to:
- select one or more worker nodes of a plurality of worker nodes to train a machine learning model in a round of training,
- wherein the one or more worker nodes are selected to optimize a performance of an updated machine learning model for a validation dataset after the round of training, wherein the updated machine learning model has one or more parameters of the machine learning model trained by the one or more worker nodes in a previous round of training.
Dependencies are inferred from the claim text.