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Content for  TS 23.288  Word version:  19.2.0

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6.2H.2.4 Inference procedure for vertical federated learning  6.2H.2.4.1 Inference procedure for vertical federated learning when NWDAF or Trusted AF is acting as VFL server  6.2H.2.4.2 Inference procedure for vertical federated learning when untrusted AF is acting as VFL server  6.2H.3 Contents of ML Model Training service for Vertical Federated Learning 
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6.2H.2.4  Inference procedure for vertical federated learning  p. 180

6.2H.2.4.1  Inference procedure for vertical federated learning when NWDAF or Trusted AF is acting as VFL server  p. 180
Reproduction of 3GPP TS 23.288, Fig. 6.2H.2.4.1-1: Inference procedure for vertical federated learning when NWDAF or Trusted AF is acting as VFL server
Figure 6.2H.2.4.1-1: Inference procedure for vertical federated learning when NWDAF or Trusted AF is acting as VFL server
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The inference procedure when trusted AF is acting as VFL server may be triggered by a request or subscription from a 5GC consumer NF or internal service logic of the AF acting as VFL server. If triggered by internal service logic of the AF acting as VFL server, the steps 0, 1,7 and 8 are skipped.
Step 0.
The analytics consumer NF sends an Analytics request/subscribe (Analytics ID, Target of Analytics Reporting= e.g. UE IDs and optionally both Analytics Reporting Information=Analytics target period and Analytics Filter, Analytics Accuracy Request if the analytics consumer NF requests Analytics Accuracy Monitoring) to NWDAF containing AnLF by invoking a Nnwdaf_AnalyticsInfo_Request or a Nnwdaf_AnalyticsSubscription_Subscribe., providing parameters as defined in clause 6.1.3.
Step 1.
If the NWDAF containing AnLF can be the VFL server to generate the VFL inference results for the requested analytics ID, then step 1 is skipped.
If the NWDAF containing AnLF can not generate the analytics output, the NWDAF containing AnLF determines the VFL Server AF for the requested analytics.
If the VFL server is trusted AF, the NWDAF containing AnLF sends a request to trusted AF VFL server using Naf_Inference_subscribe/request including Analytics ID, Target of Analytics Reporting = e.g. UE IDs and optionally Analytics Reporting Information=Analytics target period Analytics Filter and Analytics Accuracy Request.
Step 2.
Based on the information received in the step 0 or 1, VFL server decides to initiate the VFL inference procedure with the VFL clients. Before the VFL server initiate the VFL inference procedure, the VFL server may initiate the VFL training procedure if no VFL model is already trained as described in the clause 6.2H.2.3 based on the information received in the step 0 or 1 or VFL server local configuration. VFL Server selects clients(s) using information stored in the VFL training process. The server may select some or no clients, e.g. depending on the accuracy of the VFL model, the contribution to the training result and the current status of the VFL clients.
When no VFL Clients are selected, the VFL server may generates the VFL inference results based only on its local trained ML model associated with the determined VFL correlation ID, skipping the steps 2 - 6 (and 7 if step 1 was also skipped).
VFL server NWDAF or trusted AF determines and sends a VFL Inference request/subscription to each VFL client including the Target of VFL inference = e.g. UE IDs, VFL correlation ID to indicate the VFL client which previously well-trained VFL local model associated with this ID will be used and optionally VFL inference filter. Data time window: Time when intermediate local result is needed.
Step 2a.
For each NWDAF VFL client, the VFL Server NWDAF or trusted AF sends an Nnwdaf_VFLInference_Subscribe or Nnwdaf_VFLInference_Request to the VFL client.
Step 2b.
For each trusted AF VFL client, the VFL Server NWDAF sends an Naf_VFLInference_Subscribe or Naf_VFLInference_Request to the VFL client.
Step 2c.
For each untrusted AF VFL client, the VFL Server NWDAF sends an Nnef_VFLInference_Subscribe or Nnef_VFLInference_Request to the NEF serving the AF.
Step 2d.
For each untrusted AF VFL client, the NEF converts any internal identifiers to external identifiers and sends an Naf_VFLInference_Subscribe or Naf_VFLInference_Request to the untrusted AF VFL client.
Step 3.
Each VFL Client collects its local data by using the current mechanism if the VFL Client does not have local data available already.
Step 4.
Based on the VFL correlation ID, each VFL Client determines the VFL local model to generate the intermediate local inference results.
Step 5.
VFL Client sends the client intermediate local results to the VFL server.
The intermediate local results, which are sent from the VFL Client to the VFL Server during the VFL inference process, are the information for the VFL Server to combine and generate the VFL inference results.
If the VFL server used an inference subscription in step 2, step 5 may be repeated.
Step 5a.
Each NWDAF VFL client sends an Nnwdaf_VFLInference_Notify or Nnwdaf_VFLInference_Request response to the VFL Server NWDAF or trusted AF.
Step 5b.
Each trusted AF VFL client sends a Naf_VFLInference_Notify or Naf_VFLInference_Request response to the VFL Server NWDAF.
Step 5c.
Each untrusted AF VFL client sends a Naf_VFLInference_Notify or Naf_VFLInference_Request response to the NEF.
Step 5d.
For each untrusted AF VFL client, the NEF converts any external to internal identifiers and sends an Nnef_VFLInference_Notify or Nnef_VFLInference_Request response to the NWDAF VFL server.
Step 6.
The VFL server may collect its local data and generate the intermediate local inference results. When the VFL Server selected VFL clients to participate in the VFL Inference process, it combines all the intermediate local results to generate the VFL inference results based on the VFL correlation ID. The VFL server takes into account the participation of each VFL client during the ML training process and the importance of the intermediate local results when generates the combined inference output.
The VFL server may compute the VFL accuracy based on all the intermediate local results received from VFL clients and the label, if it receives Analytics accuracy request in step 0.
Step 7.
Depending on request, the NWDAF or trusted AF VFL server sends Nnwdaf_AnalyticsInfo_Response or Nnwdaf_AnalyticsSubscription_Notify or Naf_Inference_Response/Notify to the consumer (i.e. NWDAF containing AnLF) including the VFL inference results, optionally, VFL accuracy.
Step 8.
The NWDAF containing AnLF may provide VFL accuracy if the consumer NF provides Analytics Accuracy request in step 0.
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6.2H.2.4.2  Inference procedure for vertical federated learning when untrusted AF is acting as VFL server  p. 182
Reproduction of 3GPP TS 23.288, Fig. 6.2H.2.4.2-1: Inference procedure for vertical federated learning when untrusted AF is acting as VFL server
Figure 6.2H.2.4.2-1: Inference procedure for vertical federated learning when untrusted AF is acting as VFL server
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The inference procedure when untrusted AF is acting as VFL server may be triggered by a request or subscription from a 5GC consumer NF or internal service logic of the AF acting as VFL server. If triggerd by internal service logic of the AF acting as VFL server, the steps 0, 1,7 and 8 are skipped.
Step 0.
Same as step 0 in clause 6.2H.2.4.1.
Step 1.
SSame as step 1 in clause 6.2H.2.4.1, to NEF using Nnef_Inference_subscribe/request. NEF forwards the subscription request to AF using Naf_Inference_subscribe/request.
Step 2.
Same as step 2 in clause 6.2H.2.4.1, to NEF using Nnef_VFLInference_subscribe/request. An untrusted AF includes the external NWDAF ID and sends the request to the NEF.
NEF converts any received external identifiers to internal identifiers and forwards the subscription request to NWDAF using Nnwdaf_VFLInference_subscribe/request.
Step 2c.
An NWDAF VFL client may send Nnwdaf_VFLInference_Subscribe or Nnwdaf_VFLInference_Request to the VFL client to other one or more NWDAF VFL client as configured from which it desires to receive the client intermediate inference results.
Step 3.
Same as step 3 in clause 6.2H.2.4.1.
Step 4.
Same as step 4 in clause 6.2H.2.4.1.
Step 5.
Same as step 5 in clause 6.2H.2.4.1, to NEF using Nnwdaf_VFLInference_Notify/Request Response.
NEF converts any received internal identifiers to external identifiers and forwards the subscription notify to the untrusted AF using Nnef_VFLInference_Notify/Request Response.
Step 5c-5d.
The NWDAF VFL clients share the client intermediate inference results with the NWDAF VFL client from which it received request in step 2c. This NWDAF VFL client aggregates the received client intermediate inference results, performs local computation using the aggregated intermediate inference results, then sends one notify message to the NEF by including its client intermediate inference result.
If the VFL server used an inference subscription in step 2, step 5 may be repeated.
Step 6.
Same as step 6 in clause 6.2H.2.4.1.
Step 7.
Same as step 7 in clause 6.2H.2.4.1, to NEF using Naf_Inference_Notify/Request Response.
NEF converts any received internal identifiers to external identifiers and forwards the subscription Notify to NWDAF using Nnef_Inference_Notify/Request Response.
Step 8.
Same as step 8 in clause 6.2H.2.4.1.
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6.2H.3  Contents of ML Model Training service for Vertical Federated Learning  p. 183

The consumers of the ML Model training services may provide the input parameters in Nnwdaf_VFLTraining service or Naf_VFLTraining service or in Nnefe_VFLTraining service as listed below:
  • Analytics ID: identifies the analytics for which the ML Model is requested to be trained.
  • At start of Training phase VFL server sends VFL Correlation ID: identifies a VFL process to be executed among the candidate VFL participants.
  • [OPTIONAL] VFL Interoperability Information that indicates the intermediate model training information that the VFL Server supports and asks the client to verify its support for (e.g. activations, gradients, type of loss), the content of the VFL Interoperability Information is not standardized in this Release.
  • [OPTIONAL] In training phase, Maximum response time (i.e. the maximum time between VFL clients receive intermediate model training information and send back intermediate training result).
  • [OPTIONAL] Training Filter Information: enables to select which data for the ML Model training is requested, e.g. S-NSSAI, Area of Interest. Parameter types in the Training Filter Information are the same as or subset of parameter types in the ML Model Filter Information which are defined in clause 6.2A.2.
  • In training, the Intermediate model training information includes what the Server sends to each client which contains what has been agreed in the preparation phase. The content is not standardized in this Release.
  • ML model accuracy check flag: request ML model accuracy monitoring information which assists VFL server to perform ML model accuracy monitoring.
  • In the preparation phase, the initial list of samples selected by the VFL Server.
  • [OPTIONAL] At start of Training phase, sample ID of the dataset for accuracy monitoring are selected from the sample ID list decided in the preparation phase, identifying the data to be used for generating intermediate training result by VFL client and supporting the VFL Server to determine the VFL ML model accuracy.
  • [OPTIONAL] In later steps in training, the VFL server may provide delta from the sample list sent in first step of training procedure to indicate which samples aligned in the preparation phase will not be part of the rest of training procedure.
  • [OPTIONAL] Checkpoint information: indicates whether model status at the current iteration should be saved as a training checkpoint.
The VFL client provides to the consumer of the ML Model training service operations the output information in as listed below:
  • VFL Interoperability information supported by each VFL Clients.
  • In the preparation phase, the list of samples accepted by the VFL client.
  • [OPTIONAL] In training the VFL client may provide delta from the sample list received in first step of training procedure to indicate which samples will not be part of the rest of training procedure.
  • The delta list can only remove samples from the initial sample list.
  • [OPTIONAL] In preparation phase, the Feature ID(s) that the VFL client supports. A Feature ID indicates what features the VFL client can use for an Analytics ID, the values of the Feature ID are not standardized.
  • [OPTIONAL] VFL Interoperability Information that indicates the Intermediate model training information that the client supports in response to what the server sent in the subscription request.
  • In training, the Intermediate training result includes what the Client sends to the server which contains what has been agreed in the preparation phase. The content is not standardized in this Release.
  • [OPTIONAL] Local ML model accuracy monitoring information.
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