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8.5.4.2 SS_DAUsageReport API
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8.5.4.2.1 General
Service description: This API enables a VAL server (or DA client) to the usage report of digital assets from DA server.
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8.5.4.2.2 Request operation
API operation name: SS_DAUsageReport_Request Description: The consumer requests to get the usage report of digital assets. Known Consumers: VAL server, DA client. Inputs: See Table 8.5.3.1-1. Outputs: See Table 8.5.3.2-1. See clause 8.5.2.1 for details of usage of this operation.
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8.5.4.2.3 Subscribe operation
API operation name: SS_DAUsageReport_Subscribe Description: The consumer subscribes for the digital assets usage report. Known Consumers: VAL server, DA client. Inputs: See Table 8.5.3.3-1. Outputs: See Table 8.5.3.4-1. See clause 8.5.2.2 for details of usage of this operation.
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8.5.4.2.4 Notify operation
API operation name: SS_DAUsageReport_Notify Description: The consumer is notified with digital assets usage report. Known Consumers: VAL server, DA client. Inputs: See Table 8.5.3.5-1. Outputs: See Table 8.5.3.6-1. See clause 8.5.2.3 for details of usage of this operation. Annex A (informative): Deployment model...
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1 Scope
The present document specifies the procedures and information flows necessary for AIML Enablement SEAL Service.
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2 References
The following documents contain provisions which, through reference in this text, constitute provisions of the present document. - References are either specific (identified by date of publication, edition number, version number, etc.) or non‑specific. - For a specific reference, subsequent revisions do not apply. -...
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3 Definitions of terms, symbols and abbreviations
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3.1 Terms
For the purposes of the present document, the terms given in 3GPP TR 21.905 [1] and the following apply. A term defined in the present document takes precedence over the definition of the same term, if any, in 3GPP TR 21.905 [1]. AIMLE client set identifier: an identifier of the set of selected AIMLE clients. AI/ML i...
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3.2 Symbols
For the purposes of the present document, the following symbols apply: <symbol> <Explanation>
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3.3 Abbreviations
For the purposes of the present document, the abbreviations given in 3GPP TR 21.905 [1] and the following apply. An abbreviation defined in the present document takes precedence over the definition of the same abbreviation, if any, in 3GPP TR 21.905 [1]. ADAES Application Data Analytics Enablement Server AIMLE AI/ML ...
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4 Architectural requirements
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4.1 General requirements
[AR-4.1-a] The AIML enablement layer shall be able to support one or more VAL applications. [AR-4.1-b] Supported AIML enablement capabilities shall be offered as APIs to the VAL applications. [AR-4.1-c] The AIML enablement layer shall support interaction with 3GPP network system to consume network and AI/ML support s...
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4.2 AIML capability related requirements
[AR-4.2-a] The AIMLE server shall be capable of provisioning and exposing ML client information. [AR-4.2-b] The AIMLE server shall be capable of supporting the registration, discovery, and selection of AIMLE clients which participate as ML members in AIML service lifecycle. [AR-4.2-c] The AIMLE layer shall be capable...
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5 Application architecture for enabling AI/ML services
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5.1 General
The functional architecture enhancements for the AIML enablement service are based on the generic functional model specified in clause 6.2 of 3GPP TS 23.434 [5]. The architecture enhancements are organized into functional entities to describe a functional architecture enhancement which addresses the support for AIML en...
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5.2 Application enablement architecture
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5.2.1 On-Network AIML Enablement (AIMLE) Functional Architecture
Figure 5.2.1-1: On-network AIMLE functional model Figure 5.2.1-1 illustrates the on-network functional model of AIMLE. In the vertical application layer, the VAL client communicates with the VAL server over VAL-UU reference point. VAL-UU supports both unicast and multicast delivery modes. The AIMLE functional entiti...
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5.2.1.1 Service-based AIMLE architecture representation
Figure 5.2.1.1-1 exhibits the service-based interfaces for providing and consuming AIMLE services. The AIMLE server could provide service to VAL server and AIMLE client through interface SAiml. Figure 5.2.1.1-1: Architecture for AIML enablement – Service based representation. Figure 5.2.1.1-2 illustrates the servic...
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5.2.2 Off-Network AIMLE Functional Architecture
Figure 5.2.2-1: Off-network AIMLE functional model Figure 5.2.2-1 illustrates the off-network (UE-to-UE) functional model of AIML enablement. In the vertical application layer, the VAL client communicates with a further VAL client over VAL-PC5 reference point. VAL-PC5 supports both unicast and multicast delivery mod...
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5.2.3 Functional Entities Description
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5.2.3.1 General
Each subclause is a description of a functional entity and does not imply a physical entity.
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5.2.3.2 AIMLE client
The AIMLE client functional entity acts as the application client supporting AIMLE services. It interacts with the AIMLE server.
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5.2.3.3 AIMLE server
The AIMLE server functional entity provides AIMLE services supported within the vertical application layer. It interacts with the AIMLE client, the 3GPP network, other SEAL services, and VAL server.
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5.2.3.4 ML repository
The ML repository is a logical entity that serves both as a registry for ML/FL members and as a repository for application layer ML model related information. It can be accessed by the AIMLE server.
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5.2.4 Reference Points Description
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5.2.4.1 General
The reference points for the functional model related to AIMLE are described in the following subclauses.
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5.2.4.2 AIML-UU
The interactions related to AIML enablement functions between the AIMLE client and AIMLE server are supported by AIML-UU reference point. This reference point utilizes Uu reference point as described in 3GPP TS 23.401 [7] and 3GPP TS 23.501 [6].
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5.2.4.3 AIML-S
The interactions related to AIML enablement functions between the VAL server(s) and the AIMLE server are supported by AIML-S reference point.
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5.2.4.4 AIML-C
The interactions related to AIML enablement functions between the VAL client(s) and the AIMLE client within a VAL UE are supported by AIML-C reference point.
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5.2.4.5 AIML-R
The interactions related to AIML enablement functions between the AIMLE server, and the ML repository are supported by AIML-R reference point.
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5.2.4.6 AIML-E
The interactions related to AIML enablement functions between AIMLE servers (e.g., central and edge AIMLE servers). It supports: a) coordination of AIML enablement functions between AIMLE servers, including exchange of AIMLE-related information; b) federation between AIMLE servers, subject to operator policy and agre...
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5.2.4.7 AIML-PC5
The interactions related to AIML enablement functions between AIMLE clients in off-network deployments.
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6 AIMLE Functional Description
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6.1 Support for ML model retrieval
This functionality covers the retrieval of ML models by an AIMLE client or a VAL server via the AIMLE server. The AIMLE server provides the functionality required to retrieve ML models stored in a ML repository based on a filtering criteria. The functionality is provided to the ML model consumer via an API following a ...
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6.2 Support for ML model training
This functionality enables the AIMLE server to support ML model training based on requests from a VAL server. The AIMLE server provides the functionality to train a specific ML model or assist the ML model training at VAL server/client(s).
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6.3 Support for FL member registration
This functionality covers the registration, registration update and de-registration of the candidate FL member to the ML repository which is keeping the FL member registrations. Such candidate member can be a VAL server functionality or an enabler layer functionality (e.g. AIMLE server) which is registering to the ML r...
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6.4 Support for FL events subscription and notification
This functionality enables a consumer (who can be the AIMLE server or a VAL server e.g. acting as FL server) to subscribe for FL related events and getting notified on changes on the availability of the FL members which are to be used for the FL-related task (e.g., training). This capability at the ML repository acting...
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6.5 Support AI/ML task transfer
This functionality covers the AIMLE support for ML task transfer, which is applicable to scenarios where an AI/ML member cannot finish the assigned AI/ML task during the performing process. In this scenario, the AIMLE server assists the source AI/ML member by support transferring the intermediate AI/ML information (e.g...
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6.6 Support for AIMLE client registration
This functionality allows an AIMLE client (e.g., AI/ML capable UEs) to register with an AIMLE server. The AIMLE server stores the client information for future interactions. This functionality is crucial for enabling the AIMLE client to participate in AI/ML operations.
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6.7 Support for AIMLE client discovery
This functionality enables the VAL server to discover available AIMLE clients for AI/ML operations, such as training or inference. The AIMLE server provides the functionality to select suitable AIMLE clients that fulfill the discovery criteria. NOTE: AIMLE client discovery can be also used to discover VAL clients asso...
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6.8 Support for AIMLE client selection
This functionality enables the selection of AIMLE clients to participate in AI/ML operations. There are two modes for client selection: VAL server selection and AIMLE server selection. In VAL server selection, the functionality is provided by the AIMLE Server selecting candidate AIMLE clients from the client list provi...
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6.9 Support for AIMLE client participation
This functionality enables the AIMLE server to verify and manage the participation of AIMLE clients in AI/ML operations. The AIMLE client responds with its willingness to perform AI/ML operations based on the information provided by the AIMLE server.
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6.10 Support for ML model management
This functionality enables the AIMLE server to manage ML models through interaction with the ML repository. The AIMLE server provides the storage and discovery functionality for ML model information. The storage functionality allows the AIMLE server to store ML model information in the repository. The discovery functio...
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6.11 Support HFL training
This functionality provides the AIMLE server support for horizontal federated learning. This support is applicable to the case where multiple AIMLE clients are expected to locally train the ML model, and the AIMLE server is required to select, configure and coordinate the HFL clients.
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6.12 Support AIMLE client selection subscription and notification
This functionality is related to the AIMLE client selection subscription request and notification to enable VAL Servers to subscribe for monitoring AIML members who meet criteria for performing an AIMLE service, selecting AIMLE clients (associated with the VAL clients) and receiving notification when there is an update...
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6.13 Support for Split AI/ML Operation
Split AI/ML operation is a type of AI/ML operation that allows distributed processing related to ML models into multiple stages on different processing nodes. The intention is to offload the computation-intensive and energy-intensive AI/ML stages to network endpoints, whereas leave the privacy-sensitive and delay-sensi...
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6.14 Support data management assistance
This functionality covers the AIMLE assistance in data management related operations in the ML model lifecycle. AIMLE data management assistance is the process of the AIMLE server assisting AIMLE service consumers with managing data operations (data preparation and processing) performed by VAL clients.
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6.15 Support for Transfer Learning enablement
This functionality covers the support for discovering and selecting pre-trained models transfer learning operations in application enablement layer, where the support is based on the request for either an ML task from VAL layer or for an analytics task from ADAES. Transfer Learning enablement allows the consumer to dis...
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6.16 Support for FL member grouping
This functionality covers the AIMLE capability 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 FL member groups based on the AI/ML operations, which are based on 1) the analytics event/ser...
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6.17 Support vertical federated learning
This functionality is related to the AIMLE support for vertical FL (VFL) among AIMLE clients serving as VFL members. This capability involves the determination of employing VFL based on the ML model training request, the feature alignment and the decision on the AIMLE clients serving as VFL clients, based on the traini...
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6.18 Support for ML model training capability evaluation
This functionality enables AIMLE server to request AIMLE client for ML model training capability evaluation to support FL training (e.g. HFL, VFL). AIMLE client evaluates its capability and availability to join the FL training process and responds to the AIMLE server with the evaluation result (e.g. join the FL trainin...
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6.19 Support AIML service operations control and management
This functionality enables the VAL server (as AIMLE service consumer) to control the operation mode of an AIMLE service for a given AI/ML operation.
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6.20 Support for ML model update
This functionality enables the AIMLE server to update trained and deployed ML models by detecting model performance degradation, and triggering ML model re-training and update to fix the observed degradation.
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6.21 Support for ML model performance monitoring
This functionality covers a capability of AIMLE server for monitoring detecting a degradation relation to an ML operation / analytics operation and translating to an ML model degradation (expected or predicted) and performing an action to alleviate this issue (new model training or re-training).
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6.22 Support for AIMLE assisted ML model selection
This functionality enables an AIMLE service consumer to request assistance with ML model selection from an AIMLE server. The AIMLE server returns a list of ML models with corresponding model performance.
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6.23 Support for AIMLE context transfer in edge data networks
This functionality provides support for AIMLE operations performed by AIMLE clients spread across multiple edge service areas in edge data networks. The functionality allows the context transfer between edge AIMLE servers when an AIMLE client moves to a different edge service area.
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6.24 Support for Assisting Hierarchical Computing
This functionality provides support for assisting hierarchical computing by AIMLE servers. The functionality allows the consumer to request hierarchical computing assistance information to support its operations.
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6.25 Support for ML model evaluation information management
This functionality covers the management of ML models evaluation information by an AIMLE client or a VAL server via the AIMLE server. ML models evaluation information is associated with ML model(s) and is information required to enable evaluation of a ML model performance and correctness. The AIMLE server provides the ...
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6.26 Support for AIMLE server registration procedures in hierarchical AIMLE deployments
This functionality provides support for AIMLE server registration procedures in hierarchical AIMLE deployments. The functionality allows the consumer to request registration, update registration, and de-registration procedures to support hierarchical AIMLE deployments.
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6.27 Support for ML model training in hierarchical AIMLE deployments
This functionality provides support for ML model training in hierarchical AIMLE deployments. The functionality allows a central AIMLE server to obtain support from edge AIMLE servers for ML model training. The central AIMLE server can request ML model training from multiple edge AIMLE servers to fulfill requirements fo...
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6.28 Support for ML model split learning using relays
This functionality covers a capability at AIMLE for identifying and determining an intermediate entity (VAL UE) to serve as relay for a split learning operation, and particularly a split AI/ML operation. Such intermediate entity can be an application layer relay UE which either doesn’t provide any processing and is not...
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7 Identities and commonly used values
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7.1 General
The common identities for SEAL and ADAES refer to 3GPP TS 23.434 [5] and 3GPP TS 23.436 [4] respectively. The following clauses list the additional identities and commonly used values for AIMLE Service.
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7.2 AIMLE server ID
The AIMLE server ID uniquely identifies the AIML enablement server.
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7.3 AIMLE client ID
The AIMLE client ID uniquely identifies the AIML enablement client.
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7.4 ML repository ID
The ML repository ID uniquely identifies the ML-related repository function, which is used for storing the ML models and ML related information, as well as for serving as registry for the ML / FL members.
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7.5 ML model ID
The ML model ID uniquely identifies the application-layer ML model.
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7.6 FL member ID
The FL member ID uniquely identifies the participant entity in a Federated Learning process which is supported by AIMLE service, e.g., AIMLE server ID, VAL server ID or EAS ID.
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7.7 AIMLE service area
The AIMLE service area is the area where the AIML Enablement server owner provides its AIML support services. The AIMLE service area can be expressed as a Topological Service Area (e.g., a list of TA), a Geographical Service Area (e.g., geographical coordinates) or both.
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7.8 ML model profile ID
The ML model profile ID uniquely identifies a ML model profile that is created by the ML repository as a result of a successful ML model storage request by a AIMLE server.
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7.9 AIMLE client set ID
The AIMLE client set ID is a unique identifier assigned by an AIMLE server that is associated with the set of AIMLE clients that have been selected and have agreed to participate in performing AI/ML related operations, e.g., ML model training.
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8 Procedures and information flows
The security aspects for the AIMLE services are specified in 3GPP TS 33.434 [14].
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8.1 General
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8.2 ML model retrieval
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8.2.1 General
ML model retrieval enable applications consuming services from the AI/ML application enablement layer to retrieve ML models available with the enablement layer. ML model retrieval enables an AIMLE client or VAL server to obtain ML models of interest. The retrieval of ML models is based on matching ML model retrieval f...
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8.2.2 Procedure
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8.2.2.1 General
Following procedures are supported for ML model retrieval: - ML model retrieval request-response procedure; - ML model subscribe-notify procedures for retrieval of ML models, comprising: - Subscription procedure; - Subscription update procedure; and - Unsubscribe procedure;
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8.2.2.2 ML model retrieval
Figure 8.2.2.2-1 illustrates the procedure for an AIMLE client or VAL server to discover and obtain ML models from the AIMLE server. Pre-conditions: 1. The AIMLE client or VAL server has received information (e.g., URI, IP address) related to the AIMLE Server. 2. The AIMLE client or VAL server has received security ...
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8.2.2.3 ML model retrieval subscription
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8.2.2.3.1 General
Clause 8.2.2.3.2 and clause 8.2.2.3.3 together illustrate the ML model retrieval subscribe-notify ML model. Clause 8.2.2.3.4 illustrates the ML model retrieval subscription update procedure. Clause 8.2.2.3.5 illustrates the ML model retrieval unsubscribe procedure.
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8.2.2.3.2 Subscribe
Figure 8.2.2.3.2-1 illustrates the procedure for an AIMLE client or VAL server to subscribe with the AIMLE server to be notified of retrieval of ML models. Pre-conditions: 1. The AIMLE client or VAL server has received information (e.g. URI, IP address) related to the AIMLE Server. 2. The AIMLE client or VAL server ...
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8.2.2.3.3 Notify
Figure 8.2.2.3.3-1 illustrates the ML model retrieval notify operation between the AIMLE server and an AIMLE client or VAL server. Pre-conditions: 1. The AIMLE client or VAL server has subscribed for ML model retrieval with the AIMLE Server. Figure 8.2.2.3.3-1: ML model retrieval notification 1. An event occurs ...
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8.2.2.3.4 Subscription update
Figure 8.2.2.3.4-1 illustrates the procedure for an AIMLE client or VAL server to update a subscription with the AIMLE server. Pre-conditions: 1. The AIMLE client or VAL server has subscribed for ML model retrieval with the AIMLE Server. Figure 8.2.2.3.4-1: ML model retrieval subscription update 1. The requestor ...
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8.2.2.3.5 Unsubscribe
Figure 8.2.2.3.5-1 illustrates the procedure for an AIMLE client or VAL server to unsubscribe with the AIMLE server. Pre-conditions: 1. The AIMLE client or VAL server has subscribed for ML model retrieval with the AIMLE Server. Figure 8.2.2.3.5-1: ML model retrieval unsubscribe 1. The requestor (e.g., AIMLE clien...
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8.2.3 Information flows
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8.2.3.1 ML model retrieval request
Table 8.2.3.1-1 describes information elements for the ML model retrieval request from the AIMLE client or VAL server to the AIMLE server. Table 8.2.3.1-1: ML model retrieval request Information element Status Description Requestor identifier M The identifier of the requestor (e.g., AIMLE client or VAL server). ...
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8.2.3.2 ML model retrieval response
Table 8.2.3.2-1 describes information elements for the ML model retrieval response from the AIMLE server. Table 8.2.3.2-1: ML model retrieval response Information element Status Description Successful response O (NOTE 2) Indicates that the ML model retrieval request was successful. >List of ML models M List ...
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8.2.3.3 ML model retrieval subscribe request
Table 8.2.3.3-1 describes information elements for the ML model retrieval subscription request from the AIMLE client or VAL server to the AIMLE server. Table 8.2.3.3-1: ML model retrieval subscription request Information element Status Description Requestor identifier M The identifier of the requestor (e.g., AIM...
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23.482
8.2.3.4 ML model retrieval subscription response
Table 8.2.3.4-1 describes information elements for the ML model retrieval subscription response from the AIMLE server. Table 8.2.3.4-1: ML model retrieval subscription response Information element Status Description Successful response O (NOTE) Indicates that the ML model retrieval request was successful. > Su...
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8.2.3.5 ML model retrieval notification
Table 8.2.3.5-1 describes information elements for the ML model retrieval subscription response from the AIMLE server. Table 8.2.3.5-1: ML model retrieval notification Information element Status Description Subscription ID M Subscription identifier corresponding to the subscription. List of ML models O List o...
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8.2.3.6 ML model retrieval subscription update request
Table 8.2.3.6-1 describes information elements for the ML model retrieval subscription update request from the AIMLE client or VAL server to the AIMLE server. Table 8.2.3.6-1: ML model retrieval subscription update request Information element Status Description Requestor identifier M The identifier of the reques...
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8.2.3.7 ML model retrieval subscription update response
Table 8.2.3.7-1 describes information elements for the ML model retrieval subscription update response from the AIMLE server. Table 8.2.3.7-1: ML model retrieval subscription update response Information element Status Description Successful response O (NOTE) Indicates that the ML model retrieval subscription up...
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23.482
8.2.3.8 ML model retrieval unsubscribe request
Table 8.2.3.8-1 describes information elements for the ML model retrieval unsubscribe request from the AIMLE client or VAL server to the AIMLE server. Table 8.2.3.8-1: ML model retrieval unsubscribe request Information element Status Description Requestor identifier M The identifier of the requestor (e.g., AIMLE...
a0e5e9ef6b81b6012e0c5a66b182e412
23.482
8.2.3.9 ML model retrieval unsubscribe response
Table 8.2.3.9-1 describes information elements for the ML model retrieval unsubscribe response from the AIMLE server. Table 8.2.3.9-1: ML model retrieval unsubscribe response Information element Status Description Successful response O (NOTE) Indicates that the ML model retrieval unsubscribe request was success...
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8.3 ML model training
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8.3.1 General
This clause describes the procedure for supporting the ML model training by the AIMLE server. The VAL server requests the AIMLE server for the ML model training. VAL server requests the AIMLE server to train a ML model by specifying the type of learning, details about the ML model to be trained or requirements to selec...
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8.3.2 Procedure for ML model training
Figure 8.3.2-1 illustrates the procedure for AIMLE server to support ML model training based on the request from the VAL server. Figure 8.3.2-1: ML model training 1. The VAL server sends an ML model training request to AIMLE server, requesting to assist in its ML model training. This request consists of ML model in...
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8.3.3 Information flows
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8.3.3.1 ML model training request
Table 8.3.3.1-1 describes the information flow from the VAL server to the AIMLE server for requesting the ML model training. Table 8.3.3.1-1: ML model training request Information element Status Description Requestor identity M Identity of the VAL server performing the request. Training type M Identifies whet...