Content for  TS 23.288  Word version:  18.6.0

The NWDAF service consumer may subscribe or unsubscribe for training an ML Model by invoking the Nnwdaf_MLModelTraining_Subscribe/ Nnwdaf_MLModelTraining_Unsubscribe service operation. The parameters that can be provided by the NWDAF service consumer are listed in clause 6.2F.2. In order to enable Federated Learning, NWDAF Service consumer act as FL Server NWDAF can subscribe to multiple NWDAFs containing MTLF act as FL Client NWDAFs, which are selected by the FL Server NWDAF. The FL server NWDAF may use the request to check if an NWDAF can meet the ML Model training requirement (e.g. ML Model Interoperability information, Analytics ID, Serving Area and/or availability of data and time). In such case, the FL server NWDAF includes an ML Preparation Flag. When the ML Preparation Flag presents in the request, the service provider NWDAF only checks if it can meet the ML Model training requirement (e.g. ML Model Interoperability information, Analytics ID, Serving Area and/or availability of data and time) and / or can successfully download the model if the model information is provided. The FL server NWDAF may use the request to get the Model Accuracy information of the global ML Model calculated by the FL Client NWDAFs. In such cases, the service consumer NWDAF includes a Model Accuracy Check Flag. When the Model Accuracy Check Flag is present in the request, the service provider NWDAF uses the local training data as the testing dataset to calculate the Model Accuracy information of the ML Model provided by the service consumer NWDAF. When NWDAF service consumer determine to further update the ML Model, NWDAF service consumer modifies the subscription by invoking Nnwdaf_MLModelTraining_Subscribe service operation including Subscription Correlation ID with ML Model Information (as defined in clause 6.2A.2).

The NWDAF containing MTLF trains ML Model provided at step 2 by collecting new data or re-use the data that it owns. If the ML Model file is not provided in step 1, the NWDAF containing MTLF shall first get the ML Model using the information indicated at step 1.

When the NWDAF containing MTLF completes ML Model training, the NWDAF containing MTLF notifies the NWDAF service consumer with ML Model Information (as defined in clause 6.2A.2) of updated ML Model by invoking the Nnwdaf_MLModelTraining_Notify service operation. The parameters that can be provided by the NWDAF containing MTLF as service provider is specified in clause 6.2F.2. If the NWDAF containing MTLF determines to terminate the ML Model training, i.e. NWDAF containing MTLF will not provide further notifications related to this request, then the NWDAF containing MTLF may notify the NWDAF Service consumer a Terminate Request indication with cause code (e.g. NWDAF overload, not available for the FL process anymore, etc.) by invoking the Nnwdaf_MLModelTraining_Notify service operation. In order to enable Federated Learning, NWDAF containing MTLF acting as FL Client NWDAF can notify NWDAF Service consumer acting as FL Server NWDAF the local ML Model information and status report of FL training including accuracy information of local model and Training Input Data Information (e.g. areas covered by the data set, sampling ratio, maximum/minimum of value of each dimension, etc.). If the Model Accuracy Check Flag is present in the Nnwdaf_MLModelTraining_Subscribe, the service provider NWDAF acting as FL Client NWDAF may notify the NWDAF Service consumer acting as FL Server NWDAF the Model Accuracy information of the global ML Model.

The NWDAF service consumer may request the NWDAF containing MTLF to get the information about the ML Model training based on the ML Model file or ML Model information as described in clause 6.2F.2 provided by the service consumer by invoking the Nnwdaf_MLModelTrainingInfo_Request service operation. The parameters that can be provided by the NWDAF service consumer are listed in clause 6.2F.2. In order to enable Federated Learning, NWDAF Service consumer acting as FL Server NWDAF requests to get ML Model Training Information from multiple NWDAF containing MTLF acting as FL Client NWDAFs, which are selected by the FL Server NWDAF. The details are specified in clause 6.2C. The NWDAF service consumer may use the request to check if an NWDAF can meet the ML Model training requirements (e.g. ML Model Interoperability information, Analytics ID, Service Area/DNAI and/or availability of data and time). In such cases, the NWDAF service consumer includes an ML Preparation Flag. The NWDAF service consumer may use the request to get the Model Accuracy of the ML Model provided by the service consumer using local training data in the NWDAF containing MTLF as the testing dataset. In such cases, the service consumer NWDAF includes a Model Accuracy Check Flag.

When the ML Preparation Flag is present in the request, the NWDAF containing MTLF only checks whether it can meet the ML Model training requirement and/or can successfully download the model if the model information is provided. Based on the check result, the NWDAF containing MTLF gets a successful return code or failure cause code (e.g. NWDAF does not meet the ML training requirements) as the information about the ML Model training. When the Model Accuracy Check Flag is present in the request, the NWDAF containing MTLF uses the local training data as the testing dataset to calculate the Model Accuracy information of the ML Model provided by the service consumer. The NWDAF containing MTLF includes the Model Accuracy information into the information about the ML Model training. When the NWDAF containing MTLF is ongoing ML Model training based on the ML Model file or ML Model information as described in clause 6.2F.2 provided by the NWDAF service consumer, the NWDAF containing MTLF gets a failure cause code (e.g. ML training is not complete) as the information about the ML Model training. When the NWDAF containing MTLF completes ML Model training based on the ML Model file or ML Model information as described in clause 6.2F.2 provided by the NWDAF service consumer, the NWDAF containing MTLF gets a successful return code and the ML Model Information of the trained ML Model as the information about the ML Model training.

The NWDAF containing MTLF replies to the NWDAF service consumer with the information about the ML model training by invoking the Nnwdaf_MLModelTrainingInfo_Request response service operation.