In one embodiment, a first network device receives a notification that the first network device has been selected to validate a machine learning model for a second network device. The first network device receives model parameters for the machine learning model that were generated by the second network device using training data on the second network device. The model parameters are used with local data on the first network device to determine performance metrics for the model parameters. The performance metrics are then provided to the second network device.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: receiving, at a first network device, a notification that the first network device has been selected to validate a machine learning model for a second network device; receiving model parameters for the machine learning model that were generated by the second network device using training data on the second network device; using the model parameters with local data on the first network device to determine performance metrics for the model parameters; and providing the performance metrics to the second network device. 2. The method as in claim 1, wherein the machine learning model is an artificial neural network (ANN). 3. The method as in claim 2, wherein the first network device is selected to validate the ANN based on a determination that environmental conditions of the first and second network devices are similar. 4. The method as in claim 1, wherein the performance metrics comprise at least one of: a mean squared error, a precision value, or a recall value. 5. The method as in claim 1, wherein the training data on the second network device and the local data on the first network device are generated during a same time period. 6. The method as in claim 5, wherein the same time period is selected by the second network device based on available network resources. 7. The method as in claim 5, wherein the second network device reverts to using model parameters from a previous time period based on a determination that performance metrics determined by the first and second network devices diverge for model parameters associated with the same time period. 8. The method as in claim 1, wherein the first network device is selected by a network manager. 9. The method as in claim 1, wherein the first network device is selected via a validation request broadcast by the second network device to a plurality of network devices. 10. An apparatus, comprising: one or more network interfaces to communicate in a computer network as a first network device; 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: receive a notification that the first network device has been selected to validate a machine learning model for a network device; receive model parameters for the machine learning model generated by a second network device using training data on the second network device; use the model parameters with local data on the first network device to determine performance metrics for the model parameters; and provide the performance metrics to the second network device. 11. The apparatus as in claim 10, wherein the machine learning model is an artificial neural network (ANN). 12. The apparatus as in claim 10, wherein the training data on the second network device and the local data on the first network device are generated during a same time period. 13. The apparatus as in claim 11, wherein the training data on the network device and the local data are generated during a same time period. 14. The apparatus as in claim 13, wherein the network device reverts to using model parameters from a previous time period based on a determination that the performance metrics for the model parameters differ from performance metrics generated by the network device. 15. A tangible, non-transitory, computer-readable media having software encoded thereon, the software, when executed by a processor on a first network device, operable to: receive a notification that the first network device has been selected to validate a machine learning model for a network device; receive model parameters for the machine learning model generated by the network device using training data on the network device; using the model parameters with local data to determine performance metrics for the model parameters; and providing the performance metrics to the network device. 16. A method comprising: sending, by a first network device, a validation request to one or more network devices to validate a machine learning model; receiving a notification that a particular network device of the one or more network devices has been selected to perform the validation; sending model parameters for the machine learning model to the particular network device to cause the particular network device to generate performance metrics using the model parameters with a local data set; receiving the performance metrics for the model parameters; and determining whether the model parameters are valid based on a comparison between the received performance metrics and performance metrics generated by the first network device. 17. The method as in claim 16, wherein the machine learning model is an artificial neural network (ANN). 18. The method as in claim 16, wherein the model parameters are associated with a first time period, the method comprising: determining that the model parameters are not valid; and reverting to model parameters from a previous time period. 19. The method as in claim 16, further comprising: determining that the model parameters are not valid; and generating new model parameters. 20. The method as in claim 16, further comprising: determining a data collection time period to generate the model parameters based on network activity. 21. The method as in claim 16, wherein the validation request is a unicast message sent to a network manager. 22. The method as in claim 16, wherein the validation request is a broadcast message sent to a plurality of network devices. 23. 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: send a validation request to one or more network devices to validate a machine learning model; receive a notification that a particular network device has been selected to perform the validation; send model parameters for the machine learning model to the particular network device to cause the particular network device to generate performance metrics using the model parameters with a local data set; receive the performance metrics for the model parameters; and determine whether the model parameters are valid based on a comparison between the received performance metrics and locally generated performance metrics for the model parameters.