hash stringlengths 32 32 | doc_id stringlengths 5 12 | section stringlengths 5 1.47k | content stringlengths 0 6.67M |
|---|---|---|---|
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.7.2.3 Selecting UEs for reporting locations based on PEI | Figure 6.7.2.3-1: Selecting UEs for reporting locations based on PEI
1. The UE registers via NG-RAN node at an AMF.
2. The AMF obtains subscription data from the UDM that contain user consent for data collection and an indication whether the UE is suitable to serve as a PRU and whether the PRU is stationary.
3. The AMF... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.7.2.4 Inference of location for NG-RAN assisted positioning | Figure 6.7.2.4-1: Inference of location for NG-RAN assisted positioning
0. A trigger to determine location as specified in of TS 23.273 [7] occurs at an LMF, for instance an incoming Nlmf_Location_DetermineLocation service operation. The LMF determines to use NG-RAN assisted direct AIML positioning with LMF side model.... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.7.2.5 Inference of location for UE assisted positioning | Figure 6.7.2.5-1: Inference of location for UE assisted positioning
0. A trigger to determine location as specified in of TS 23.273 [7] occurs at an LMF, for instance an incoming Nlmf_Location_DetermineLocation service operation. The LMF determines to use UE assisted direct AIML positioning with LMF side model.
1. The ... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.7.2.6 Monitoring accuracy of model for NG-RAN assisted location measurements | Figure 6.7.2.6-1: Monitoring accuracy of model for NG-RAN assisted location measurements
1.-10. Same steps as in Figure 6.7.2.1-1. The model training entity is replaced by the model accuracy monitoring entity, which may be the same as the model training entity or a different entity, for instance an LMF or NWDAF/MTFLF. ... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.7.3 Impacts on services, entities and interfaces | Model training entity (LMF or NWDAF/MTLF):
NOTE: The entity performing the model training is expected to be selected in the conclusion phase.
- Performs a bulk subscription for location model training measurements towards the AMF.
- Receive measurements and ground truth location.
- Train model.
- Store input data and t... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.8 Solution #8: MTLF-based model performance monitoring for AI/ML positioning | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.8.1 Description | This solution addresses Key Issue #1 "Enhancements to LCS to support Direct AI/ML based Positioning" regarding how to monitor model performance for ML models used for direct AI/ML based positioning. And assumes LMF with an AI/ML model for inference capability.
According to the definition of TS 23.288 [5], an NWDAF cont... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.8.2 Procedures | Figure 6.8.2-1: Procedure of MTLF-based model performance monitoring for AI/ML positioning
1. The AF/Consumer NF subscribes to the LMF to request AI/ML Positioning.
2. The LMF performs UE location data collection from UE, RAN, AMF and GMLC as described in TS 23.273 [7].
Editor's note: The details on enhancements to tho... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.8.3 Impacts on services, entities and interfaces | MTLF:
- Supports to monitor AI/ML positioning model performance.
- Supports to generate new or re-trained AI/ML positioning models.
LMF:
- Supports to provide inference of AI/ML positioning to MTLF.
RAN:
- Supports to provide UE location data to LMF.
UE:
- Supports to provide UE location data to LMF. |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.9 Solution #9: new solution for KI#1 support monitoring the performance of AI model | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.9.1 Description | This solution resolves the how to monitor and evaluate the performance of an AI/ML models in Key Issue #1 Enhancements to LCS to support Direct AI/ML based Positioning. PRU (Positioning Reference Unit) defined for LCS service is reused to support LMF to monitor and evaluate the performance of an AI/ML model.
In this so... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.9.2 Procedures | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.9.2.1 Performance of AI/ML model monitoring based on PRU | Figure 6.9.2.1-1 performance of AI/ML model monitoring based on PRU
1. AMF receives LCS service request from UE (i.e. MO-LR), or from LCS Client (i.e. MT-LR), or AMF itself.
2-3. AMF selects a LMF and sends Location request to LMF that determines to select AI/ML model based positioning method to compute the final UE lo... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.9.2.2 Procedure of PRU information collection for AI/ML model training | Figure 6.9.2.2-1: Procedure of PRU information collection for AI/ML model training
1. NWDAF invoke an Nnrf_NFDiscovery Request service operation to an NRF. The service operation includes a PRU existence indication and an AoI.
2. The NRF selects one or more PRU serving LMFs based on the PRU existence indication and the ... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.9.3 Impacts on services, entities and interfaces | Editor's note: It is FFS to capture impacts on existing 3GPP nodes and functional elements.
NWDAF:
- Support to discover the PRU serving LMFs, and request the PRU known location and associated PRU location measurement(s) directly from the PRU serving LMFs.
LMF:
- Support for AI/ML Model inference to compute the final U... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.10 Solution #10: Direct AI/ML based positioning with NWDAF assistance | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.10.1 Description | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.10.1.1 General | This solution addresses Key Issue #1 "Enhancements to LCS to support Direct AI/ML based Positioning".
In this solution, the existing NWDAF framework as specified in TS 23.288 [5] is reused for Direct AI/ML positioning in 5GC as follows:
- The NWDAF containing MTLF trains the ML model for Direct AI/ML positioning.
- The... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.10.1.2 Input data | To train the ML model, the NWDAF containing MTLF collects UE positioning related input data as list in Table 6.10.1.2-1.
Table 6.10.1.2-1: Data collected by NWDAF for training the ML model for UE positioning
Information
Source
Description
UE ID
LMF
SUPI of the target UE.
Timestamp
LMF
A time stamp when the location was... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.10.2 Procedures | Figure 6.10.2-1: Procedure for NWDAF providing ML model for direct UE positioning
1a. [In the case of 5GC-MT-LR procedure] The AF or LCS client sends a request to the GMLC for positioning of a target UE, and the GMLC invokes the Namf_Location_ProvidePositioningInfo service operation towards the AMF, as specified in TS ... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.10.3 Impacts on services, entities and interfaces | NWDAF:
- The NWDAF containing MTLF trains the ML model for Direct AI/ML positioning.
- The NWDAF containing MTLF collects UE positioning related data from e.g. the LMF, AF and AMF, to (re)train the ML model for Direct AI/ML positioning.
LMF:
- Requests or subscribes to ML model for Direct AI/ML positioning from the NWD... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.11 Solution #11: Data collection procedure for LMF-side model training | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.11.1 Functional Description | This solution proposes to reuse the UE positioning procedure defined in TS 23.273 [7] to identify target LMF(s) for measurement data collection. After receiving the data collection request, the LMF(s) can send the measurement data and PRU information to NWDAF for LMF-side model training directly(i.e. without passing GM... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.11.2 Procedures | Figure 6.11.2-1: Procedure of data collection for LMF-side model training
1. NWDAF sends measurement data/PRU information collection request to GMLC with target UE ID(s) and its NF instance ID.
Editor's note: Whether to perform data collection per UE, per area or both is FFS.
2. The GMLC invokes a Nudm_UECM_Get service... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.11.3 Impacts on existing services, entities and interfaces | To implement the solution above, the NWDAF shall provide its NF instance ID and target UE ID(s) in the training data collection request. The GMLC shall forward the request to AMF and then AMF shall forward the request to LMF. The LMF needs to prepare training data according to the received target UE ID(s) and send the ... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.12 Solution #12: new solution for KI#1 support the data collection for AI model training based on authorization | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.12.1 Description | This solution resolves how to trigger or authorize the positioning data collection for AI/ML model training for Key Issue #1 Enhancements to LCS to support Direct AI/ML based Positioning. In this solution, it proposes to define a subscription data of authorization for positioning data collection of a UE for AI/ML mode ... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.12.2 Procedures | Figure 6.12.2-1: Data collection for AI/ML mode training with authorization
1. NWDAF subscribes the data collection for AI model training service to LMF, to collect the measurements data/location data of one UE when the LCS Service is triggered in future.
If NWDAF has the target UE ID(s), e.g. SUPI, or GPSI, then NWDAF... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.12.3 Impacts on services, entities and interfaces | Editor's note: It is FFS to capture impacts on existing 3GPP nodes and functional elements.
LMF:
- Support for AI/ML Model inference to compute the final UE location.
- Check with UDM whether the positioning data of the UE is allowed for AI/ML model training.
UDM:
- Enhanced to authorize the positioning data collection... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.13 Solution #13: Sample/Feature alignment and general training procedure for the VFL | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.13.1 Description | This solution is proposed to address Key Issue #2: 5GC Support for Vertical Federated Learning and based on the Use Case #5.
In Use Case #5, two scenarios are identified:
- Scenario 1: NWDAF initiates VFL training process.
- Scenario 2: AF initiates VFL training process.
This solution focuses on scenario 2 where AF is ... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.13.2 Procedures | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.13.2.1 Sample and feature alignment procedure | In VFL, it is required VFL Active Participant(VFL server) and VFL Passive Participant(VFL client) have same samples and different features before the model training. This solution proposed a preparation process used to have a negotiation between VFL Active Participant(VFL server) (i.e. AF) and VFL Passive Participant(V... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.13.2.2 General training procedure for the VFL between the VFL Active Participant(VFL server) (i.e. AF) and VFL Passive Participant(VFL client) (i.e. NWDAF) | In the process of VFL model training, the AF and NWDAF(s) exchange intermediate data (e.g. intermediate training result, loss information). It is assumed that the VFL Active Participant(VFL server) has ground truth data or labels or is able to collect ground truth data or labels. It is also assumed that the VFL Passive... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.13.2.3 Inferencing procedure for the VFL between the VFL Active Participant (i.e. AF) and VFL Passive Participant (i.e. NWDAF(s)) | Figure 6.13.2.3-1
1. VFL Active Participant(VFL server) (i.e. AF) may determine to perform VFL inference using the VFL well-trained ML model which is represented by VFL model correlation ID. When there is not a VFL Passive Participant available for VFL inference which has been involved for VFL training of the VFL model... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.13.3 Impacts on services, entities and interfaces | NWDAF:
- Enhancement to support the VFL model sample and feature alignment with the AF to make sure the sample is the same, but the feature for the sample is different between AF and NWDAF.
- Based on the model and loss function provided by the AF, performing local training and update for the VFL model.
NEF:
- Enhancem... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.14 Solution #14: General procedure for NWDAF initiated Vertical Federated Learning between NWDAF(s) and AF(s) | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.14.1 Description | This clause specifies how NWDAF(s) and AF(s) can leverage VFL technique to train joint ML model to address the Key Issue #2: 5GC Support for Vertical Federated Learning. The training data is always a key element for ML model. In general, more data used in model training, better performance the model would achieve. Due ... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.14.2 Procedures | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.14.2.1 Training Procedure | The figure 6.14.2.1-1 below shows general training procedure for Vertical Federated Learning between NWDAF(s) and AF(s).
Figure 6.14.2.1-1: General training procedure for VFL between NWDAF(s) and AF(s)
0. The consumer sends a subscription request to VFL server NWDAF to train an ML model, including Analytics ID, ML mode... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.14.2.2 Inference Procedure | Figure 6.14.2.2-2: General inference procedure for VFL between NWDAF(s) and AF(s)
1. The first step is the same as the existing one captured in clause 6.4.4 of TS 23.288 [5] since the NF consumer doesn't need to be aware whether the NWDAF performs VFL or normal analytics for the requested analytics ID. Consumer NF send... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.14.3 Impacts on services, entities and interfaces | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.14.3.1 Training Impact | NWDAF:
- Supports selecting the VFL clients and executing the VFL procedure.
- Supports performing sample alignment.
NRF:
- Supports the registration and discovery of entities which participate in VFL. |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.14.3.2 Inference Impact | NWDAF:
- Enhanced to initiate the VFL inference procedure to support the request analytics.
- Collects the intermediate results from AF and generates the final inference result based on the AF and NWDAF intermediate inference result.
NEF:
- Enhanced to support the VFL inference related information exchange between NWDA... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.15 Solution 15: Support for vertical federated learning: Model Training and Inference | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.15.1 Description | Editor's note: This clause will describe the solution principles and architecture assumptions for corresponding key issue(s). Sub-clause(s) may be added to capture details.
Vertical federated learning or feature-based federated learning is applicable to the cases that two data sets share the same sample space but diffe... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.15.2 Procedures | Editor's note: This clause describes high-level procedures and information flows for the solution. |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.15.2.1 Sample/Feature alignment | Feature/Sample alignment is supported by a VFL server receiving a model training request for VFL operation. The VFL Server on reception of a ML training request may perform the following tasks:
- Identify other VFL function(s) that perform VFL operation based on the model requested.
- Identify the data availability (fe... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.15.2.2 VFL training procedure | After the alignment is complete the VFL active participant coordinates the VFL training process. The task of the VFL active participant during the training process are proposed to be:
- Train the model containing the label data based on intermediate values
- Provide Gradient/Losses to Passive Participants
- Determine c... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.15.2.3 Distributed Inference | Once a model consumer (i.e. an AnLF) is informed that the a model is trained using VFL, the consumer sends a inference request to the VFL Active Participant. The VFL active participant sends inference requests to each VFL participant and aggregates the received result to derive an aggregate inference output. The VFL ac... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.15.3 Impacts on services, entities and interfaces | Editor's note: This clause captures impacts on existing services, entities and interfaces.
MTLF supports:
- New functionality to support alignment of samples for the VFL process and selecting participants for the VFL training process.
- Coordinating the VFL training by acting as an Active Participant.
- Joining a VFL t... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.16 Solution #16: Support for VFL with NWDAF and AF as Participants | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.16.1 Description | This solution addresses use case #4 and key issue #2. Unlike traditional centralized learning approaches, where data is pooled together in a single location, or Horizontal Federated Learning (HFL), where different entities contribute similar types of data about different samples, VFL allows for the collaborative traini... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.16.2 Procedures | NOTE 1: In this solution, the VFL server coordinates the VFL operation and acts as active participant with access to labels.
NOTE 2: VFL clients in this solution can be either VFL active participants with access to labels or VFL passive participants without access to labels.
NOTE 3: In this solution, NWDAF instances co... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.16.3 Impacts on services, entities and interfaces | NWDAF:
- New service to support data alignment.
- Enhancements to Nnwdaf_MLModelTraining service (or new service(s)) to support VFL preparation as well as sharing of intermediate training and inference results.
NEF/AF:
- New service to support data alignment.
- New service(s) to support VFL preparation as well as shari... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.17 Solution #17: NF discovery and selection for VFL | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.17.1 Description | The VFL process may include NWDAF and external AF. This solution proposes to do NF discovery process through NRF. The NWDAF can report/update its VFL capability to NRF and external AF can report its VFL capability to NRF via NEF. After reporting the VFL capability, the VFL server can select VFL client afterwards. Below... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.17.2 Procedures | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.17.2.1 External AF VFL capability report | The 2-side VFL capability reporting process of external AF can be conducted as follows:
Figure 6.17.2-1: VFL capability report for External AF
1. External AF reports or updates its VFL capability(i.e. VFL server, VFL client, VFL server and client) and its AF ID to NEF.
2. NEF store the VFL capability and AF ID of exter... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.17.2.2 NWDAF VFL capability report | The 2-side VFL capability reporting process of NWDAF can be conducted as follow:
Figure 6.17.2-2: VFL capability report for NWDAF
1. NWDAF reports/updates its VFL capability (i.e. VFL server, VFL client, VFL server and client) to NRF in its NF profile.
2. NRF stores the NF profile of NWDAF.
3. The NRF response with ope... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.17.2.3 Client selection process of VFL | The client selection process of VFL can be conducted as follows:
Figure 6.17.2-3: Client selection process of VFL
0. The procedure above assumes the VFL server selection is done, or no need for VFL server selection.
1. The VFL Server NWDAF discovers and selects other NWDAF(s) containing MTLF as VFL Client NWDAF(s) from... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.17.3 Impacts on existing services, entities and interfaces | To implement the solution above, The AF and NWDAF shall report its VFL capability to NRF. The NEF shall be involved in helping external AF for the VFL capability reporting. On the other side, the VFL server shall have the ability to select VFL client(s) base on the criteria mentioned in clause 6.17.2.3. The NRF shall h... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.18 Solution #18: Vertical Federated Learning between NWDAF and AF | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.18.1 Description | This solution is for Key Issue#2: 5GC Support for Vertical Federated Learning.
If an ML model needs to be trained on local data set(s) from data source(s) (e.g. NF and AF), which have different feature spaces for the same samples (e.g. UE IDs), and some data cannot be obtained by the model training logic function direc... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.18.2 Procedures | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.18.2.1 Discovery and selection of VFL clients | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.18.2.1.1 Discovery and selection of AF(s) if NWDAF as the VFL server | Figure 6.18.2.1.1-1: Registration and discovery of AF(s) for VFL
1-3. The AF registers its NF profile as defined in clause 5.2.7.2.2 of TS 23.502 [3], with the difference it includes the Supported VFL capability information (VFL capability type (e.g. VFL Server (VFL active participants) and/or VFL Clients (VFL passive ... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.18.2.1.2 Discovery and selection of NWDAF(s) if AF as the VFL server | Figure 6.18.2.1.2-1: Registration and discovery of NWDAF for VFL
1-3. The NWDAF registers its NF profile as defined in clause 5.2.7.2.2 of TS 23.502 [3], with the difference it includes the Supported VFL capability information (VFL capability type (e.g. VFL Server (VFL active participants) and/or VFL Clients (VFL passi... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.18.2.2 VFL training procedure between NWDAF and AF | The following principles are defined for the VFL Joint ML Model training procedures:
- If AF is involved in the VFL Joint ML Model training process, it is assumed that such AF is capable to perform model training.
- If NEF is required for the interaction with 3rd party AFs, NEF services are extended in order to properl... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.18.2.2.1 VFL training procedure if NWDAF acts as the VFL server | Figure 6.18.2.2.1-1: VFL training procedure when NWDAF acts as the VFL server
1. If an ML model needs to be trained on local data set(s) from data source(s) (e.g. NF and AF), which have different feature spaces for the same samples (e.g. UE IDs), the NWDAF as the VFL server determines to use Vertical Federated Learning... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.18.2.2.2 VFL training procedure if AF acts as the VFL server | Figure 6.18.2.2.2-1: VFL training procedure when AF acts as the VFL server
1. If the ML model needs to be trained on local data set(s) from data source(s) (e.g. NF and AF), which have different feature spaces for the same samples (e.g. UE IDs), AF as the VFL server determines to use Vertical Federated Learning mechanis... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.18.2.3 VFL training procedure among NWDAFs | The following principles are defined for the VFL Joint ML Model training procedures:
- An NWDAF (VFL Server) with VFL Capability for performing VFL ML Training can initiate the process VFL joint Training Process.
NOTE 1: The procedures illustrated in 6.18.2.3-1 show the NWDAF (VFL Server), i.e. Active VFL Participant, ... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.18.2.4 VFL inference procedure between NWDAF and AF | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.18.2.4.1 VFL inference procedure if NWDAF acts as the VFL server | Figure 6.18.2.4.1-1: VFL inference procedure when NWDAF acts as the VFL server
1. To obtain analytics, the NF consumer sends analytics info request to NWDAF with analytics ID, Target of Analytics Reporting and analytics filter.
Editor's note: Whether the NWDAF in this step is the NWDAF acted as VFL sever in VFL model t... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.18.2.3.2 VFL inference procedure if AF acts as the coordinator | Figure 6.18.2.4.2-1: VFL inference procedure when AF acts as the VFL server
1. To obtain analytics, the NF consumer sends analytics info request to AF with analytics ID, Target of Analytics Reporting and analytics filter.
2. For the analytics info request, if the AF as the VFL server decides to do inference based on th... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.18.2.5 VFL inference procedure among NWDAFs | Figure 6.18.2.5-1 illustrated the procedure for analytics subscription at an NWDAF with an Active participant role (i.e. VFL Server) and the generation of the analytics output based on VFL inference process.
NOTE: The procedure in Figure 6.18.2.5-1 supports the Use Case #4.
The proposed procedure for VFL inference is b... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.18.3 Impacts on services, entities and interfaces | NWDAF:
- Determine to use Vertical Federated Learning mechanism to train ML model if some features cannot be obtained directly from data producer AF.
- Discover and select AF to participate the Vertical Federated Learning procedure.
- Interact with AF(s) to perform VFL training and VFL inference.
AF:
- Determine to use... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.19 Solution #19: VFL inference procedure between NWDAF and AF | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.19.1 Description | This solution is proposed for KI#2 and Scenario 2 (i.e. AF initiated Scenario) of use case#5 to support the VFL inference procedure between NWDAF and AF. In the VFL model inference, the ML models are aligned, "VFL Passive Participant" NF(s), i.e. NWDAF, send intermediate results to "VFL Active Participant" NF, i.e. AF.... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.19.2 Procedures | Figure 6.19.2-1: VFL model inference on AF initiated scenario
This clause describes the AF initiated VFL inference procedure to use ML model that trained in the VFL model training procedure. This procedure supports the two cases: case 1) the AF was involved in the VFL model training procedure, and case 2) AF was not in... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.19.3 Impacts on Existing Nodes and Functionality | For AF:
- support to request the intermediate results of local ML models from NWDAF (s).
- support to align the ML model between NWDAF.
For NWDAF:
- support to compute the intermediate results, report the intermediate results to the AF. |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.20 Solution #20: Inference procedure for the Vertical Federated Learning between NWDAF(s) and AF(s) | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.20.1 Description | This solution resolves KI#2: 5GC Support for Vertical Federated Learning, focusing on the procedures for VFL Inference.
While each party in HFL uses the trained global model to make inferences, the parties in VFL have to collaborate to make inferences and each party may have only a sub-model.
The ML Model Training for ... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.20.2 Procedures | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.20.2.1 Inference Procedure Initiated by the NWDAF | Figure 6.20.2.1-1: Inference procedure for the VFL initiated by NWDAF
0. Each NWDAF containing AnLF and AF which support VFL Inference is registered to the NRF with its NF Profile, which includes Analytics ID(s), Address information of NWDAF, Service Area, VFL Inference capability and Time interval supporting VFL Infer... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.20.3 Impacts to Services, Entities and Interfaces | NWDAF:
- Support new Service operation between NWDAF(s) containing AnLF and/or AF(s) for VFL Inference (e.g. Nnwdaf_MLInferenceInfo_Request/Response, Nnwdaf_MLInference_Subscribe/Notify).
- New capability indication for AnLF (VFL Inference Server, VFL Inference Client).
- MTLF can provide the ML Model for VFL Inference... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.21 Solution #21: Vertical Federated Learning for support of Application Layer QoE | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.21.1 Key Issue mapping | This solution addresses KI#2 "5GC Support for Vertical Federated Learning". |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.21.2 Description | Application Layer Quality of Experience (QoE) is affected by factors in different domains, such as network information (e.g. slice performance, applied policies), UE information (UE power consumption status, trajectory, user context) and Application Layer information (applied codec, QoE metric). The challenge is to use... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.21.2.1 VFL for Application QoE provisioning | In this model of VFL, sub-models running in the network and the AF generates customized analytics based on an on-going session to influence the AF and UE application to optimize QoE experienced by the user. The process can be carried by either AF as active participant or NWDAF as active participant. The ML models are s... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.21.2.2 VFL for Network parameters provisioning | In this model of Vertical Federated Learning, sub-models running in the network and AF generates customized analytics to pre-provision the network to support future QoE requirements of the user. The active participant (AF or NWDAF) performs network provisioning based on custom analytics generated by a ML algorithm oper... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.21.3 Procedures | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.21.3.1 Application provisioning for customized QoE | Figure 6.21.3.1-1: Call flow for application QoE provisioning
1. AF initiates the preparation phase for VFL for analytics ID Collaborative Analytics. This phase includes alignment on UE ID(s) and negotiation of active participant role. This step is always initiated by the AF irrespective of whether AF is the active par... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.21.3.2 Network parameters provisioning | Figure 6.21.3.2‑1: Call flow for network parameter provisioning
1. AF initiates the preparation phase for VFL for analytics ID Collaborative Analytics. This phase includes alignment on UE ID(s) and negotiation of active participant role. This step is always initiated by the AF irrespective of whether AF is the active p... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.21.4 Impacts on services, entities and interfaces | AF: Initiates VFL UE ID alignment:
- As active participant:
- Provisions bottom layer processing information;
- provisions gradients during training phase;
- receives intermediate results.
- As passive participant:
- Receives bottom layer processing information;
- Receives gradients during training phase;
- Exposure of... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.22 Solution #22: Vertical Federated learning considering internal NWDAF architecture | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.22.1 Description | This solution addresses key issue 2 and use cases 4 and 5.
It assumes that vertical federated learning is controlled by an NWDAF acting as server (or active participant). Clients (or passive participants) can either be other NWDAFs or AFs. No separate Controller is assumed.
NOTE 1: This solutions assumes that the NWDAF... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.22.2 Procedures | |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.22.2.1 VFL model training and inference involving MTLF, AnLF, ADRF and AF | Figure 6.22.2.1-1: VFL model training and inference involving MTLF, AnLF, ADRF and AF
1. The AnLF acting as VFL inference server registers its capability to act as VFL inference server in the NRF together with Analytics IDs it supports and a vendor ID.
2. The AnLF acting as VFL inference client registers its capability... |
c8f50ee24a14933c33290076ea3625ad | 23.700-84 | 6.22.2.2 VFL sample alignment for UE samples | Figure 6.22.2.2-1: VFL sample alignment for UE samples
1. Before starting a vertical federated learning, the MTLF acting as VFL training server determines an initial list of target UEs to be used as sample for vertical federated learning.
For each candidate UE, steps 2 to 11 are performed.
2. The MTLF acting as VFL tra... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.