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a968fed150b6156e01512b97375c462a | 23.700-82 | 8.16.1 Solution description | This solution enables a subscriber/consumer (who can be the coordinator FL server or API invoker entity) for subscribing for AI/ML model related events and getting notified on changes on the availability of the FL members which are to be used for ML model training.
This solution consists of the following procedures 1) ... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.16.1.1 Procedure on subscription for FL related events | This procedure describes the subscription for events related to FL member availability.
Pre-conditions:
1. The subscribing entity has the authorization to subscribe for the FL-related events.
Figure 8.16.1.1-1: Procedure for FL-related event subscription
1. The subscribing entity sends an event subscription request to ... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.16.1.2 Procedure on FL related event notification | This procedure describes the event notification procedure for the FL member availability. This can be triggered based on a change at the availability / capabilities of the candidate or selected FL members.
Pre-conditions:
1. The subscription procedure is performed by the subscribing entity.
2. The candidate/selected or... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.16.1.3 List of FL-related Events | A list (not exhaustive) of the FL related events is provided in Table 8.16.1.3-1.
Table 8.16.1.3-1: List of FL related events
Events
Events Description
Availability/Unavailability of candidate FL member(s)
The event type relates to the availability or unavailability indication of a candidate FL member or a set of membe... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.16.2 Architecture Impacts | The application enabler layer architecture impacts are the following:
- An FL member Registry is introduced for enabling the registration of candidate FL members.
NOTE 1: FL member Registry can be within A-ADRF in scenarios where AIML enablement co-exists with ADAES.
NOTE 2: FL member Registry and the ML model reposito... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.16.3 Corresponding APIs | This subclause provides a summary on the corresponding API for solution #16.
- FL event Subscription API (request-response model; API provider: FL member registry; known consumers: VAL server, AIML enablement server, API invoker; corresponding to clause 8.16.1.1).
- FL event Notify API (subscribe-notify model; API prov... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.16.4 Solution evaluation | This solution addresses Key Issue #3 and introduces the capability to support FL event notifications for FL members (or a group of FL members). Such FL members can be VAL UEs or VAL servers which are candidate to be used in ML operations. This solution is important for scenarios where the availability of candidate FL m... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.17 Solution #17: AIML operational management | |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.17.1 Solution description | The following clauses specify procedures, information flows, and APIs for Key Issue #3 to support AIML operational management. |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.17.1.1 AIML operational management procedure | This clause describes the overall AIML operational management procedure which can be used by VAL Servers to offload single or multi-step processing to the AIML enablement layer, e.g., single request resulting in AIML Enablement server performing FL client selection followed by UE local data collection monitoring and pr... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.17.1.2 AIML enablement client task triggering | This clause describes how several different procedures can be used iteratively in the AIML Enablement layer to perform AIML operational management, as described in clause 8.17.1 step 6.
AIML enablement clients may be triggered by the AIML Enablement Server to perform AIML enablement operations as follows:
a. Monitor da... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.17.2 Architecture Impacts | This solution assumes an architecture as detailed in clauses 7.2.1 or 7.2.3. There are no further impacts to the architecture based on this solution. |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.17.3 Corresponding AIML-S APIs | Table 8.17.3-1 shows the request for the AIML operational management procedure from VAL Server to the AIML Enablement Server.
Table 8.17.3-1: Request for AIML operational management procedure
Information element
Status
Description
Requestor identifier
M
The identifier of the requestor.
Security credentials
M
Security c... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.17.5 Solution evaluation | The solution for operational management provides a mechanism for the VAL layer to offload processing and/or monitoring of AIML operations to the AIML layer. This solution allows AIML enablement layer to provide significant value to VAL applications which require support with overall optimization of the AIML workflow an... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.18 Solution #18: Supporting VFL in Enablement Layer | |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.18.1 Solution description | The following clauses specify procedures, information flows and APIs for KI #4 to support VFL among Application Layer multiple UEs.
Pre-conditions:
- VFL member (e.g., AI/ML Enablement Client which is deployed to an UE) has dataset or can access to data sources or is able to collect data from data producers for the AI/... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.18.2 Architecture Impacts | This solution is based on the architecture described in clause 7. The application enabler layer architecture impacts are the following:
- Interactions between AIMLE Server, AIMLE Client, and consumer (e.g. VAL Server) are introduced to support evaluate the capability and availability of VFL members to join VFL training... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.18.3 Corresponding APIs | This subclause provides a summary on the corresponding API for solution #18.
- AIMLE Register API (request-response model; API provider: AIMLE Server or ML Repository; known consumers: AIMLE Client) to support AIMLE Client registers its VFL profile.
- AIMLE ML Model Training Capability Evaluation API (request-response ... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.18.4 Solution evaluation | This solution addresses Key Issue #4 and introduces procedures, information flows and APIs to support VFL among VAL UEs. The produce introduced in this solution can be used to evaluate the capability and availability of VFL member to join a VFL training process, and for sample and feature alignment for VFL. This soluti... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.19 Solution #19: Support for AIML operation splitting | |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.19.1 Solution description | This solution is for Key Issue #5 and addresses AIML operation splitting management and configuration.
In this solution, the term "split operation pipeline" is used to represent distributed compute nodes configured sequentially. Data can be provided at the entry point of the pipeline, and sequentialy processed through ... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.19.2 Procedure for AIML operation splitting discovery | Pre-conditions:
- AIML inference models are available in the ML model repository or A-ADRF;
- the AIML enablement server is aware of or can discover compute nodes available for performing AIML inference using available models;
Figure 8.19.2-1: Support for Split AIML operation discovery
0. An AIML VAL client needs suppo... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.19.2.1 Procedure for subscribe-notify for split operation pipeline events | Figure 8.19.2.1-1: Split operation pipeline event subscription
1. The requestor (e.g., AIML enablement client or VAL server) sends a request to subscribe to split operation pipeline events to the AIML enablement server. The request includes requestor identifier, security credentials, events for which the requestor is s... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.19.2.2 Procedure for VAL server AIML split operation registration | Figure 8.19.2.2-1: Split operation pipeline registration
1. The VAL server sends a split operation register request to the AIMLE server; the registration indicates the split operation capabilities of the VAL server. The request includes the VAL server AIML split operation profile and an expiration time. The AIML split ... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.19.2.3 Procedure for client creating pipeline | Figure 8.19.2.3-1 illustrates the procedure for AIML enablement client to create the pipeline based on the discovered nodes. The AIML enablement client has received list of nodes from the AIML enablement server as specified in the clause 8.19.2. Based on the AIML model, split points and discovered nodes, the AIML enabl... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.19.3 Architecture Impacts | The solution architecture impacts are:
- The AIML Enablement Server and AIML Enablement Client are introduced to support split operation discovery. |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.19.4 Corresponding APIs | |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.19.4.1 API Overview | This clause provides a summary on the corresponding API for solution #19.
- Discover AIML split operation profiles API (request/response model: API provider: AIMLE Server, known consumers: AIMLE Client, corresponding to step 1 to 3 of clause 8.19.2).
- Discover AIML split operation API (request/response model: API prov... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.19.4.2 Information flows | Table 8.19.4.2-1 shows the request sent by a AIML enablement client to the AIML enablement server for split operation discovery request.
Table 8.19.4.2-1: Split operation discovery request
Information element
Status
Description
Requestor identifier
M
The identity of the requestor (e.g., VAL client ID, AIML client ID, U... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.19.5 Solution evaluation | The proposed solution addresses Key Issue #5 for AIML operation splitting management and configuration.
The solution introduces a procedure for enabling discovery of AIML split operation pipelines based on VAL client requirements, procedures for subscribing to split operation events notifications at the AIMLE client an... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.20 Solution #20: AIML Enabler support for Transfer Learning | This solution addresses Key Issue #6 on transfer learning enablement.
In this study, AIML enablement can be used for two types of ML tasks:
- ADAE analytics tasks (so, the training of the model is bound to a certain analytics ID).
- VAL related tasks (so, the VAL server/client are AI/ML enhanced applications which requ... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.20.1 Solution description | Figure 8.20.1-1 illustrates the procedure where the TL enablement is performed based on the request for either an ML task from VAL layer or for an analytics task from ADAES. Such TL enablement allows the consumer to discover the similar ML models to be used as base models for the TL, as well as to support the selection... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.20.2 Architecture Impacts | The application enabler layer architecture impacts are the following:
- AIMLE server is introduced with a capability to provide support for TL enablement (discovery and selection of models to be used as pre-trained models for a certain task).
- An ML registry/repository needs to be defined for serving a database for th... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.20.3 Corresponding APIs | This subclause provides a summary on the corresponding API for solution #20.
- Discover pre-trained model info API (request / response model; API provider: AIMLE Server; known consumers: VAL server or ADAES; corresponding to step 1 and 6 of clause 8.20.1.1).
- Fetch pre-trained model info API (request / response model;... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.20.4 Solution evaluation | This solution addresses Key Issue #6 and introduces the capability to support TL enablement at AIML enablement server by supporting the discovery and selection of models to be used as pre-trained models for an ML task related to either ADAE analytics or a VAL request. This solution is necessary if TL is employed, since... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.21 Solution #21: AIML data management procedure | |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.21.1 Solution description | The following clauses specify procedures, information flows, and APIs for Key Issue #3 to support AIML data management. In this context, the term ”data management” (for AIML) refers to one of the following: data collection, data preparation, and exploratory data analysis.
Data collection is the process of obtaining raw... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.21.2 Architecture Impacts | This solution is based on client-server architecture as described by clause 7.2. |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.21.3 Corresponding APIs | Table 8.21.3-1 describes the API for the data management subscription request.
Table 8.21.3-1: AIML data management subscription request
Information element
Status
Description
AI/ML Enablement Consumer Identity
M
The identity of the AI/ML Enablement Consumer sending the subscription request.
Data management type
M
An i... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.21.4 Solution evaluation | This solution addresses key issue #3 as one or more data management (e.g., data collection, data preparation, and exploratory data analysis) steps are preconditions to any type of ML training.
The data management solution provides a mechanism for the VAL layer to offload managing and monitoring of AIML data operations ... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.22 Solution #22: Horizontal Federated Learning training | |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.22.1 Solution description | The following clauses specify procedures, information flows, and APIs for Key Issue #3 to support horizontal federated learning training procedure.
Figure 8.22.1-1: Procedure for HFL training
1. A VAL server makes a HFL training subscription request. The request includes: AIML model and model parameters, a model parame... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.22.2 Architecture Impacts | This solution is based on client-server architecture as described by clause 7.2. |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.22.3 Corresponding APIs | Table 8.22.3-1 describes the API for Horizontal FL training subscription request.
Table 8.22.3-1: Horizontal FL training subscription request
Information element
Status
Description
VAL server ID
M
The VAL server identifier.
AIML model and model parameters
M
Information about the AIML model and model parameters to use f... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.22.4 Solution evaluation | This solution addresses key issues 3, 5, and 6. The solution can be used for both Horizontal FL training and inferencing as well as in split and transfer learning scenarios.
The solution is different than solutions for Vertical FL, since HFL clients train with the same model and update the HFL server with model paramet... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.23 Solution #23: AIML services in edge | |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.23.1 Solution description | The solution addresses Key Issue #1 to enhance the architecutre and related functions to support application layer AI/ML services in edge computing scenarios. The solution also addressed Key Issue #5 to support AIML operation management splitting in edge computing scenairos.
Figure 8.23.1-1 Example deployment in edge s... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.23.2 Architecture Impacts | The application enabler layer architecture impacts are the following:
- AIMLE Server is enhanced to support the distribution of AI/ML service request in edge data networks.
- AIMLE Server is enhanced to support the aggregation of AI/ML service notifications from the edge data networks.
NOTE: A reference point between A... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.23.3 Corresponding AIML-S APIs | The step 2 AIML service request is one of several AIML-S messages described in other solutions and enhanced to provide parameters used by the Cloud AIML Enablement Server to determine how to distribute the request to edge, e.g.: identifiers of the required Edge AIML Enablement Servers, AIML operational management descr... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.23.4 Corresponding AIML-UU APIs | The step 8 AIML service notification is one of several AIML-UU messages described in other solutions, such as:
- The AIML client notifications/responses in step 6 of Figure 8.17.1-1 (Solution #17).
- The AIML Enablement clients service operation response in step 3 of Figure 8.11.2-1 (Solution #11). |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.23.5 Solution evaluation | This solution leverages other solutions (e.g., AIML operational management procedure) to describe enhancements for Cloud AIML Enablement Servers APIs. The parameters provided by the enhanced APIs are used to determine how to distribute the request to the edge.
This solution can be further complemented by solutions for ... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.24 Solution #24: Dynamic ML model distribution | |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.24.1 Solution description | This solution is for Key Issue #3 and addresses AIML model distribution dynamically, to the selected FL/ML clients where in the list of FL/ML clients that participate in the federated or distributed learning can potentially change over a period of time and more frequently based on the member selection information provi... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.24.2 Architecture Impacts | The application enabler layer architecture impacts are the following:
- AIML Enablement server is introduced to provide support the dynamic distribution of ML model to the AIML members selected for federated/distributed learning. |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.24.3 Corresponding APIs | Table 8.24.3-1 shows the details of the model distribution request sent by VAL server to an AIML enablement server.
Table 8.24.3-1: Model distribution request
Information element
Status
Description
Member selection information
M
Information related to selection of the FL/ML clients. This could include the member select... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.24.4 Solution evaluation | This solution addresses the Key issue #3, by introducing the procedure to dynamically distribute the model and the related information to the AI/ML members selected for federated/distributed learning. This solution is significant for scenarios where the availability of the potential FL members changes over a period of ... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.25 Solution #25: Support for AIML model distribution | |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.25.1 Solution description | This solution is for Key Issue #5 and addresses AIML model distribution (e.g., transfer) management and configuration; the solution also addresses Key Issue #3 (open issue #5) on distribution of the model information to FL members.
A UE may need to switch an AIML model adaptively based on task and environment variation... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.25.2 Procedures | |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.25.2.1 Subscription for AIML model updates | This solution assumes that AIML models are stored in the Model Repository, and each AIML model is associated with information identifying and describing the characteristics of the AIML model, this information is called the AIML model profile in this solution.
NOTE 1: The AIML model profile definition and cardinality pr... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.25.2.2 Notification for AIML model updates | Pre-conditions:
- The AIML enabler client has successfully subscribed for AIML models updates with the AIML enabler server.
- The AIML enable server has successfully subscribed for AIML models updates with the Model Repository.
Figure 8.25.2.2-1: Notification for AIML model updates procedure
0. The Model Repository sen... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.25.3 Architecture Impacts | The solution architecture impacts are:
- The AIML Enablement Server and AIML Enablement Client are introduced to support AIML model distribution.
- The Model Repository is introduced to store the AIML models. |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.25.4 Corresponding APIs | |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.25.4.1 API Overview | This clause provides a summary on the corresponding API for solution #25.
- Discover AIML model profiles API (subscribe/notify model: API provider: AIMLE Server, known consumers: AIMLE client, corresponding to step 1, 4 and 5 of clause 8.25.2.1).
- Discover AIML models API (subscribe/notify model: API provider: AIMLE C... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.25.5 Solution evaluation | The proposed solution addresses Key Issue #5 for AIML model distribution. The solution introduces the subscribe/notify procedures for AIML model discovery.
The solution allows a VAL client to discover, be notified ans obtain AIML model(s) that may be provided by the AIMLE server; the discovered models may be used for p... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.26 Solution #26: Support for FL member grouping | This solution addresses Key Issue #3 on FL member grouping.
The mechanism proposed in this solution is to enable the group management of the entities serving as FL clients at the application enablement layer. Such group management is about the creation, monitoring and update of the groups based on the AI/ML operations,... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.26.1 Solution description | The requirement for grouping the FL members by the VAL server (indirectly) or by the AIML enablement server (acting as consumer) is applicable to a specific VAL request or ML model ID or analytics event/ID.
Figure 8.26.1-1 illustrates the procedure for supporting the FL member grouping.
Pre-conditions:
1. VAL server is... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.26.2 Architecture Impacts | The application enabler layer architecture impacts are the following:
- AIML enablement server is introduced to provide support for triggering the grouping FL members.
- An FL member registry (which can be part of ML repository) needs to be enhanced to maintain and update group identity for an FL member group.
NOTE: Wh... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.26.3 Corresponding APIs | This subclause provides a summary on the corresponding API for solution #26.
- FL Group Management API (request / response model; API provider: AIMLE server; known consumer: VAL server; corresponding to step 3 of clause 8.26.1.1).
- Fetch FL member / group API (request / response model; API provider: ML repository; kno... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.26.4 Solution evaluation | This solution addresses Key Issue #3 and introduces the capability to support FL member grouping. This solution requires Sol #9 as pre-condition since the candidate FL members needs to be registered at the FL member repository.
This solution is feasible and doesn't introduce any dependency to 3GPP network systems.
NOTE... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.27 Solution #27: New ADAE Analytics for Supporting FL Member (re-) selection | |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.27.1 General | The following clauses specify procedures, information flows and APIs for KI #2 to enhance ADAE by introducing new Analytics for supporting FL member (re)selection. |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.27.2 Procedure | |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.27.2.1 Subscribe-notify model | Figure 8.27.2.1-1: ADAES support for application layer AI/ML Member capability analytics
0. The analytics consumer (e.g. VAL Server, AI/ML Enablement Server) sends analytics subscription request for application layer AI/ML Member capability analytics to ADAE server. For analytics subscription request, the request conta... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.27.2.2 Request-response model | Pre-conditions:
- ADAE server already have the analytics data derived from steps 3-8 in the procedure introduced in clause 8.27.2.1.
Figure 8.27.2.2-1: ADAES support for application layer AI/ML Member capability analytics
1. The analytics consumer (e.g. VAL Server, AI/ML Enablement Server) sends a request message to th... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.27.3 Information flows | |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.27.3.1 General | The following information flows are specified for application layer AI/ML Member capability analytics based on clause 8.27.2. |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.27.3.2 Application Layer AI/ML Member capability analytics subscription request | Table 8.27.3.2-1 describes the information flow from the consumer (e.g. VAL server, AI/ML Enablement server) as a request or update request for the application layer AI/ML Member capability analytics.
Table 8.27.3.2-1: Application Layer AI/ML Member capability analytics subscription request
Information element
Status
D... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.27.3.3 Application Layer AI/ML Member capability analytics subscription response | Table 8.27.3.3-1 describes the information elements for the application layer AI/ML Member capability analytics subscription response from the ADAE server to the consumer.
Table 8.27.3.3-1: Application layer AI/ML Member capability analytics subscription response
Information element
Status
Description
Result
M
The resu... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.27.3.4 Application layer AI/ML Member capability analytics notification | Table 8.27.3.4-1 describes the information flow from the ADAES to the consumer (e.g. VAL Server, AI/ML Enablement Server) as a response for the application layer AI/ML Member capability analytics.
Table 8.27.3.4-1: Application layer AI/ML Member capability analytics notification
Information element
Status
Description
A... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.27.3.5 Application Layer AI/ML Member capability data collection subscription request | Table 8.27.3.5-1 describes information elements for the application layer AI/ML Member capability data collection subscription request from the ADAE server to the Data Producer at the ADAE client or the A-ADRF.
Table 8.27.3.5-1: Data collection subscription request
Information element
Status
Description
Requestor ID
M
... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.27.3.6 Application Layer AI/ML Member capability data collection subscription response | Table 8.27.3.6-1 describes information elements for the Data collection subscription response from the application layer AI/ML Member capability data Producer at the ADAE client or the A-DCCF to the ADAE server.
Table 8.27.3.6-1: Data collection subscription response
Information element
Status
Description
Result
M
The ... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.27.3.7 Data Notification | Table 8.27.3.7-1 describes information elements for the Data Notification from the Data Producer to the ADAE server.
Table 8.27.3.7-1: Data notification
Information element
Status
Description
Data Collection Event ID
M
The identifier of the data collection event.
Data Producer ID
M
The identity of Data Producer.
Analyt... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.27.3.8 Get analytics data request | Table 8.27.3.8-1 describes information elements for the Get application layer AI/ML Member capability analytics request from the analytics consumer to the ADAE server.
Table 8.27.3.8-1: Get analytics data request
Information element
Status
Description
Requestor ID
M
The identifier of the consumer.
Analytics ID
M
The id... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.27.3.9 Get analytics data response | Table 8.27.3.9-1 describes information elements for the Get application layer AI/ML Member capability analytics response from the ADAE server to the consumer.
Table 8.27.3.9-1: Get analytics response
Information element
Status
Description
Result
M
The result of the analytics data request (positive or negative acknowled... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.27.4 Architecture Impacts | This solution is based on the architecture described in clause 7. The architecture impacts are mainly the enhancement of ADAES to support an additional analytics service for application layer AI/ML Member capability analytics. |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.27.5 Corresponding APIs | This subclause provides a summary on the corresponding API for solution #27.
- Application Layer AI/ML Member Capability Analytics API (request-response or subscribe-notify model; API provider: ADAES; known consumers: VAL server, AIMLE server).
- Application Layer AI/ML Member Capability related Data Collection APIs.
N... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.15.6 Solution evaluation | This solution addresses Key Issue #2 (open issues 2, 3, 4, and 5) and introduces a new Application Layer AI/ML Member Capability analytics to support for supporting FL member (re)selection. This solution is re-using Solution #13 for the interaction between AIMLE server and ADAES, and mainly enhances ADAES. This solutio... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.28 Solution #28: Support AI/ML Splitting Operations in Enablement Layer | |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.28.1 General | There are many different types of split AI/ML operations, for example discover nodes for split learning or split inference, AI/ML task (e.g. learning or inference) split, AI/ML task delivery, ML model distribution or delivery, AI/ML data distribution or delivery. For example, in split learning, multiple split nodes sys... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.28.2 Procedures for supporting assistance of split AI/ML operations | |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.28.2.1 Procedure for subscribe/request assistance of split AI/ML operations | Figure 8.28.2.1-1: Procedure for subscribe/request assistance of split AI/ML operations
Figure 8.28.2.1-1 illustrates the procedure for subscribe/request assistance of split AI/ML operations. The corresponding procedure in detail is as follows:
1. The Consumer (e.g. VAL Server, VAL client (e.g. via the AIML enablement ... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.28.2.2 Procedure for assistance of AI/ML task/model/data delivery/distribution | Figure 8.28.2.2-1: Procedure for assistance of AI/ML task/model/data delivery/distribution
Figure 8.28.2.2-1 illustrates the procedure for assistance of AI/ML task/model/data delivery/distribution. This procedure enhances the architecture and related functions to support management and/or configuration for in-time tran... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.28.3 Architecture Impacts | The application enabler layer architecture impacts are the following:
- AI/ML Enablement Server is introduced to support assistance of split AI/ML operations. |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.28.4 Corresponding APIs | This subclause provides a summary on the corresponding API for solution #28.
- AIMLE Assistance of Split AI/ML Operations API (request-response or subscribe-notify model; API provider: AIMLE Server; known consumers: VAL Server, VAL client (e.g. via the AIML enablement client)). |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.28.5 Solution evaluation | This solution addresses Key Issue #5 and introduces procedures on supporting various split AI/ML operations in Enablement Layer. This solution reuses existing mechanisms of 5GC (e.g., PDTQ on recommended time windows for AI/ML operations with QoS, QoS monitoring, network analytics provided by NWDAF), and ADAE analytics... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.29 Solution #29: Enhance AIML Services for Assisting Edge Computing | |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.29.1 General | An AI/ML task can be seen as a specical computing task, and edge comptuting can be used for completing the AI/ML tasks. A CAS, CES, ECS, EES or EAS (which are defined in 3GPP TS 23.558 [14]) may have different roles in different types of edge computing, e.g. hiearchitical computing (with one root node (e.g., CAS, EAS),... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.29.2 Procedures for assisting edge computing | Figure 8.29.2-1: Procedure for assisting an edge computing process
Figure 8.29.2-1 illustrates the procedure for subscribe/request assistance of edge computing. The corresponding procedure in detail is as follows:
1. The Consumer (e.g. CAS, EAS) subscribes/requests to the AI/ML Enablement Server for assistance of an ed... |
a968fed150b6156e01512b97375c462a | 23.700-82 | 8.29.3 Architecture Impacts | There is no impact on architecture in application enabler layer. Based on the existing architecture, the impacts on functionality are the following:
- AI/ML Enablement Server is introduced to assist edge computing.
- AI/ML Enablement Consumer (e.g. CAS, EAS) is introduced to consume the enhanced AI/ML Enablement Server... |
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