Machine learning model parameter transfer
AssigneeLenovo (United States) Inc.
InventorVenkata Srinivas Kothapalli
Life of this patent
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Abstract
Various aspects of the present disclosure relate to a node for wireless communication, comprising: at least one memory; and at least one processor coupled with the at least one memory and operable to cause the node to: obtain a machine learning (ML) model comprising of a set of model parameters; for each model parameter in the set of model parameters, determine a sensitivity of the model parameter, and provide error protection to the model parameter based on the sensitivity of the model parameter, the sensitivity of the model parameter indicating a degree of toleration for a value of the model parameter to vary without performance of the ML model degrading beyond a tolerance threshold value; and transmit the error protected model parameters to a further node.
Claims19
A node for wireless communication, comprising:
- at least one memory;
- and
- at least one processor coupled with the at least one memory and operable to cause the node to:
- obtain a machine learning (ML) model comprising of a set of model parameters;
- for each model parameter in the set of model parameters, determine a sensitivity of the model parameter, and provide error protection to the model parameter based on the sensitivity of the model parameter, the sensitivity of the model parameter indicating a degree of toleration for a value of the model parameter to vary without performance of the ML model degrading beyond a tolerance threshold value;
- and
- transmit the error protected model parameters to a further node.
A processor for wireless communication, comprising:
- at least one controller coupled with at least one memory and operable to cause the processor to:
- obtain a machine learning (ML) model comprising of a set of model parameters;
- for each model parameter in the set of model parameters, determine a sensitivity of the model parameter, and provide error protection to the model parameter based on the sensitivity of the model parameter, the sensitivity of the model parameter indicating a degree of toleration for a value of the model parameter to vary without performance of the ML model degrading beyond a tolerance threshold value;
- and
- output the error protected model parameters for transmission to a further node.
A method performed at a node, the method comprising:
- obtaining a machine learning (ML) model comprising of a set of model parameters;
- for each model parameter in the set of model parameters, determining a sensitivity of the model parameter, and providing error protection to the model parameter based on the sensitivity of the model parameter, the sensitivity of the model parameter indicating a degree of toleration for a value of the model parameter to vary without performance of the ML model degrading beyond a tolerance threshold value;
- and
- transmitting the error protected model parameters to a further node.
Dependencies are inferred from the claim text.