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 request/response model or a subscribe/notify model.

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).

This functionality covers the registration and registration update 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 repository/registry to act as FL member for a given application event (analytics event or event triggered by a VAL layer application server).

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 as an AIML service registry supports the subscription for events related to FL members and the notification to the consumer in case of changes. This feature assumes that such FL members (AIMLE or VAL server or AIMLE clients) have previously registered to this registry their availability and capabilities.

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., the intermediate AI/ML operation status and results) to another AI/ML member (target AI/ML member) for further operations to complete the AI/ML task.

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.

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.

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 provided by the VAL Server. In AIMLE server selection, the functionality is provided by the AIMLE Server selecting candidate AIMLE clients based on the client selection criteria provided by the VAL Server from the AIMLE clients in the ML repository.

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.

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 trained ML model information in the repository. The discovery functionality enables the AIMLE server to retrieve information about available ML models via an API following a request/response model.

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.

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 and receiving notification when there is an update on the selected and re-selected AIML client's status when re-selection is performed according to AIML member selection criteria.

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-sensitive stage at the end device as described in TS 22.261. The following functionality is provided to support split AI/ML operation:

• An application consuming services from the AI/ML application enablement layer can discover or manage (e.g., create, update, delete) a split operation profile with the AIMLE server for the purpose of consuming results from corresponding instance of a split AI/ML operation pipeline.
• A VAL server can register with the AIMLE server to indicate its capabilities for acting as a processing node of an instance of a split AI/ML operation pipeline.
• An application consuming services from the AI/ML application enablement layer can subscribe with the AIMLE server to receive event notifications related to an instance of a split AI/ML operation pipeline.

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.

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 discover the similar ML models to be used as base models for the TL, as well as to support the selection of the best model to be used as pre-trained model.

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/service by ADAES or 2) the VAL requirement for FL support services.

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 training capability evaluation functionality.

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 training process with test result, or not join with fail reason). The ML model training capability evaluation result can be used by the AIMLE server to select FL members for FL training process (e.g. HFL, VFL).

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.

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.

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).

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.

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.