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US 9,413,779 B2Granted
Learning model selection in a distributed network
AssigneeCisco Technology, Inc.
InventorsJean-Philippe Vasseur, Andrea Di Pietro, Javier Cruz Mota
Life of this patent
Estimating the term…
Abstract
In one embodiment, local model parameters are generated by training a machine learning model at a device in a computer network using a local data set. One or more other devices in the network are identified that have trained machine learning models using remote data sets that are similar to the local data set. The local model parameters are provided to the one or more other devices to cause the one or more other devices to generate performance metrics using the provided model parameters. Performance metrics for model parameters are received from the one or more other devices and a global set of model parameters is selected for the device and the one or more other devices using the received performance metrics.
Claims16
A method comprising:
- generating local model parameters by training a machine learning model at a device in a computer network using a local data set;
- identifying, at the device, one or more other devices in the network that have trained machine learning models using remote data sets that are similar to the local data set;
- transmitting, by the device, the local model parameters to the one or more other devices to cause the one or more other devices to generate performance metrics using the transmitted model parameters:
- receiving, at the device, the performance metrics from the one or more other devices;
- receiving, at the device, model parameters from the one or more other devices that were generated by the one or more other devices training one or more other machine learning models;
- using, by the device, the received model parameters with the local data set to generate local performance metrics;
- comparing, by the device, the local performance metrics with the performance metrics received from the one or more other devices to select a global set of model parameters;
- selecting, by the device, the global set of model parameters for the device and the one or more other devices based on the comparison between the local performance metrics and the received performance metrics;
- and
- selecting, by the device, the model parameters having the highest average performance metrics as the global set of model parameters.
7 more elements
An apparatus, comprising:
- one or more network interfaces to communicate in a computer network;
- a processor coupled to the network interfaces and configured to execute one or more processes;
- and
- a memory configured to store a process executable by the processor, the process when executed operable to:
- generate local model parameters by training a machine learning model using a local data set;
- identify one or more other devices that have trained machine learning models using remote data sets that are similar to the local data set;
- transmit the local model parameters to the one or more other devices to cause the one or more other devices to generate performance metrics using the transmitted model parameters;
- receive the performance metrics from the one or more other devices;
- receive model parameters from the one or more other devices that were generated by the one or more other devices training one or more other machine learning models:
- use the received model parameters with the local data set to generate local performance metrics;
- compare the local performance metrics with the performance metrics received from the one or more other devices to select a global set of model parameters;
- select the global set of model parameters for the device and the one or more other devices based on the comparison between the local performance metrics and the received performance metrics;
- and
- select the model parameters having the highest average performance metrics as the global set of model parameters.
11 more elements
A tangible, non-transitory, computer-readable media having software encoded thereon, the software when executed by a processor operable to:
- generate local model parameters by training a machine learning model using a local data set;
- identify, for the device, one or more other devices that have trained machine learning models using remote data sets that are similar to the local data set;
- transmit, for the device, the local model parameters to the one or more other devices to cause the one or more other devices to generate performance metrics using the transmitted model parameters;
- receive, at the device, the performance metrics from the one or more other devices;
- receive model parameters from the one or more other devices that were generated by the one or more other devices training one or more other machine learning models;
- use the received model parameters with the local data set to generate local performance metrics
- compare the local performance metrics with the performance metrics received from the one or more other devices to select a global set of model parameters:
- select the global set of model parameters for the device and the one or more other devices based on the comparison between the local performance metrics and the received performance metrics:
- and
- select the model parameters having the highest average performance metrics as the global set of model parameters.
7 more elements
Dependencies are inferred from the claim text.