Security for AI / ML model storage and sharing

Abstract

Embodiments include methods of a consumer network function (NFc) of a communication network. The method includes sending a first request for a first access token associated with a machine learning (ML) model to a first NF of a communication network. The first request includes at least one of an analysis identifier (ID) and an interoperability ID associated with the ML model. The method includes receiving a first response including a first access token from the first NF and sending a second request for the ML model to a producer NF (NFp) of the communication network. The second request includes the first access token and at least one of the analysis ID and the interoperability ID. The method includes receiving, from the NFp, a second response including one or more of: an ML model; an identifier of the ML model; and an address of a storage resource associated with a second NF of the communication network from which the ML model can be obtained.

Description

Technical Field

[0001] The present application relates generally to the field of communication networks, and more specifically to techniques for protecting artificial intelligence / machine learning (AI / ML) models used to generate analytics in communication networks (e.g., 5G core networks). Background Art

[0002] The fifth generation (5G) cellular systems are currently being standardized within the 3rd Generation Partnership Project (3GPP). NR was developed to provide maximum flexibility to support multiple and distinct use cases. These use cases include enhanced mobile broadband (eMBB), machine type communications (MTC), ultra-reliable low latency communications (URLLC), sidelink device-to-device (D2D), and several others.

[0003] At a high level, the 5G system (5GS) consists of an access network (AN) and a core network (CN). The AN provides connectivity from the UE to the CN, for example via base stations (e.g., gNB or ng-eNB) described below. The CN includes various network functions (NFs) that provide a wide range of different functions (e.g., session management, connection management, charging, authentication, etc.).

[0004] Figure 1 A high-level view of an exemplary 5G network architecture is shown, which consists of a Next Generation Radio Access Network (NG-RAN, 199) and a 5G Core (5GC, 198). The NG-RAN may include one or more gNodeBs (gNBs) connected to the 5GC via one or more NG interfaces, such as gNBs (100, 150) connected via corresponding interfaces (102, 152). More specifically, the gNBs may be connected to one or more access and mobility management functions (AMFs) in the 5GC via corresponding NG-C interfaces, and to one or more user plane functions (UPFs) in the 5GC via corresponding NG-U interfaces. The 5GC may include various other network functions (NFs), such as a session management function (SMF).

[0005] In addition, the gNBs may be connected to each other via one or more Xn interfaces, e.g., an Xn interface (140) between gNBs (100, 150). The radio technology used for NG-RAN is generally referred to as "New Radio" (NR). With respect to the NR interface to the UE, each gNB may support frequency division duplex (FDD), time division duplex (TDD), or a combination thereof. Each gNB may provide service for a geographic coverage area comprising one or more cells, and in some cases may also use various directional beams to provide coverage in the corresponding cells. In general, a DL "beam" is a coverage area of ​​a network sent reference signal (RS) that can be measured or monitored by a UE.

[0006] NG-RAN 199 is layered into Radio Network Layer (RNL) and Transport Network Layer (TNL). The NG-RAN architecture (i.e., NG-RAN logical nodes and the interfaces between them) is defined as part of the RNL. For each NG-RAN interface (NG, Xn, F1), the relevant TNL protocols and functions are specified. TNL provides services for user plane transport and signaling transport.

[0007] The NG RAN logical node (e.g., gNB 100) includes a central unit (CU or gNB-CU, e.g., 110) and one or more distributed units (DU or gNB-DU, e.g., 120, 130). The CU is a logical node that hosts high-layer protocols and performs various gNB functions (e.g., controls the operation of the DU). The DU is a decentralized logical node that hosts low-layer protocols and may include various subsets of gNB functions depending on the functional split options. Each CU and DU may include various circuits required to perform its respective functions, including processing circuits, communication interface circuits (e.g., transceivers), and power supply circuits.

[0008] gNB-CU through the corresponding F1 logical interface (for example, Figure 1 The F1 interface is connected to one or more gNB-DUs (see interfaces 122 and 132). However, a gNB-DU can be connected to only a single gNB-CU. The gNB-CU and its connected gNB-DUs are visible only as gNBs to other gNBs and the 5GC. In other words, the F1 interface is not visible outside the gNB-CU.

[0009] Another change in 5G networks (e.g., 5GC) is that the traditional peer-to-peer interfaces and protocols in previous generations of networks are modified and / or replaced by a service-based architecture (SBA), in which a network function (NF) provides one or more services to one or more service consumers. Typically, various services are self-contained functions that can be changed and modified in an isolated manner without affecting other services. In addition, these services include various "service operations", which are finer-grained divisions of the overall service functionality. The interaction between service consumers and producers can be of "request / response" or "subscribe / notify" type.

[0010] A 5GC NF of particular interest in this disclosure is the Network Data Analysis Function (NWDAF). This NF provides network analysis information (e.g., statistics of past events and / or prediction information) to other NFs at the network slice instance level. The NWDAF can collect data from any 5GC NF. Note that a "network slice" is a logical partition of a 5G network that provides specific network capabilities and features, e.g., to support a specific service. A network slice instance is a collection of NF instances and the network resources (e.g., compute, storage, communication) required to provide the capabilities and features of a network slice.

[0011] Machine learning (ML) is a type of artificial intelligence (AI) that focuses on using data and algorithms to mimic the way humans learn, gradually improving in accuracy as more data becomes available. ML algorithms build a model based on sample (or "training") data, which is then used to make predictions or decisions. ML algorithms can be used in a variety of applications (e.g., medicine, email filtering, speech recognition, etc.) where it is difficult or infeasible to develop conventional algorithms to perform the desired task. A subset of ML is closely related to computational statistics.

[0012] The 5G system architecture allows any NF to obtain analytical data from the NWDAF using the Data Collection Coordination Function (DCCF) and the associated Ndccf services. The NWDAF can also store and retrieve analytical information from the Analytics Data Repository Function (ADRF). 3GPP TS 23.288 (v17.2.0) specifies that the NWDAF is the primary NF for calculating analytical reports, and classifies the NWDAF into two sub-functions (or logical functions): the Analysis Logic Function (AnLF), which performs the analysis process; and the Model Training Logic Function (MTLF), which performs training and retraining of the ML model used by the AnLF. Summary of the invention

[0013] AI / ML models (or more simply, ML models) are generally considered important intellectual property of their owners (e.g., 5GC vendors), and therefore their confidentiality and integrity need to be protected at all times. 3GPP is studying the feasibility of sharing or storing ML models in network equipment that may be provided by different vendors. Under this arrangement, the ML model should be protected from being accessed and used by consumer NFs provided by a different vendor than the ML model. However, no specified solution for this requirement currently exists.

[0014] Embodiments of the present disclosure are directed to addressing these and related issues, problems, and / or difficulties, thereby facilitating further advantageous deployments of ML models for network analysis.

[0015] Some embodiments of the present disclosure include methods (eg, processes) for a consumer NF (NFc) of a communication network (eg, 5GC).

[0016] The exemplary methods include sending a first request for a first access token associated with an ML model to a first NF of a communication network. The first request includes one or more of the following items associated with the ML model: an analysis ID and an interoperability ID. The exemplary methods also include receiving a first response from the first NF including the first access token. The exemplary methods may also include sending a second request for the ML model to a producer NF (NFp) of the communication network. The second request includes the first access token, and at least one of the analysis ID and the interoperability ID. The exemplary methods also include receiving a second response from the NFp including one or more of the following items: the ML model; an identifier of the ML model; and an address of a storage resource associated with a second NF of the communication network, from which the ML model can be obtained.

[0017] In some embodiments, the first NF is a Network Repository Function (NRF). In other embodiments, the first NF is an Analytical Data Repository Function (ADRF). In some embodiments, the second NF is a NFp. In other embodiments, the second NF is an ADRF. In some embodiments, one or more of the following apply: NFc is a NWDAF (AnLF), and NFp is a NWDAF (MTLF).

[0018] Other embodiments include exemplary methods (eg, processes) for NFp of a communication network (eg, 5GC).

[0019] The exemplary methods include registering information associated with an ML model in an NRF of a communication network. The ML model is generated, owned and / or maintained by the NFp. The registration information associated with the ML model includes an analysis ID and an interoperability ID. The exemplary methods also include encrypting the ML model and sending a first request for storing the encrypted ML model to an ADRF of the communication network. The first request includes the encrypted ML model or a first address of a storage resource associated with the NFp, from which the ML model can be obtained.

[0020] In some embodiments, the exemplary methods may further include receiving a second request for the ML model from the NFc of the communication network. The second request includes the first access token, and at least one of the analysis ID and the interoperability ID. The exemplary methods may further include: based on verifying the first access token, sending a second response to the NFc, the second response including one or more of the following items: the ML model; an identifier of the ML model; a first address of a storage resource associated with the NFp; and a second address of a storage resource associated with the ADRF, from which the ML model can be obtained.

[0021] In some embodiments, one or more of the following applies: NFc is NWDAF (AnLF), and NFp is NWDAF (MTLF).

[0022] Other embodiments include methods (eg, processes) for NRF of a communication network (eg, 5GC).

[0023] The exemplary methods may include registering information associated with an ML model generated, owned, and / or maintained by an NFp of a communication network. The registered information associated with the ML model includes an analysis ID and an interoperability ID. The exemplary methods may also include receiving a first request for a first access token associated with the ML model from an NFc of the communication network. The first request includes at least one of the analysis ID and the interoperability ID. The exemplary methods may also include sending a first response including the first access token to the NFc.

[0024] In some embodiments, these exemplary methods may further include receiving a second request for a second access token from the first NF of the communication network. The second request includes at least one of the analysis ID and the interoperability ID and one of the following:

[0025] - a first address of a storage resource associated with the NFp, from which the ML model can be obtained; or

[0026] - A second address of a storage resource associated with the ADRF of the communication network, from which the ML model can be obtained.

[0027] The example methods may also include sending a second response including a second access token to the first NF.

[0028] In some of these embodiments, the first address of the storage resource associated with the NFp is a first universal resource locator (URL), and the second address of the storage resource associated with the ADRF is a second URL or a fully qualified domain name (FQDN). In some of these embodiments, the first NF is NFc. In other of these embodiments, the first NF is NFp.

[0029] Other embodiments include methods (eg, processes) for ADRF of a communication network (eg, 5GC).

[0030] The exemplary methods may include receiving a first request from a NFp of a communication network for storing an encrypted ML model. The first request includes the encrypted ML model or a first address of a storage resource associated with the NFp, from which the encrypted ML model can be obtained. The exemplary methods may also include storing the encrypted ML model in a storage resource associated with the ADRF. The exemplary methods may also include sending a first response to the NFp, the response including a second address of a storage resource associated with the ADRF.

[0031] In some embodiments, the first NF is a NFp. In other embodiments, the first NF is a NFc of a communication network. In some embodiments, the NFc is a NWDAF (AnLF) and / or the NFp is a NWDAF (MTLF). In some embodiments, the first address of the storage resource associated with the NFp is a first URL, and the second address of the storage resource associated with the ADRF is a second URL or FQDN.

[0032] Other embodiments include NFc, NFp, NRF and ADRF (or network nodes hosting such NFs) configured to perform operations corresponding to any of the exemplary methods described herein. Other embodiments also include non-transitory computer-readable media storing computer-executable instructions that, when executed by a processing circuit, configure such network nodes or NFs to perform operations corresponding to any of the exemplary methods described herein.

[0033] These and other disclosed embodiments may provide various benefits and / or advantages. Embodiments improve the security of confidential and / or sensitive ML models by providing owners / producers of ML models with the ability to protect ML models during various transmission, storage, and retrieval scenarios, thereby facilitating the deployment of such models in multi-vendor communication networks (e.g., 5GC).

[0034] These and other objects, features and advantages of the present disclosure will become apparent when the following detailed description is read in conjunction with the accompanying drawings which are briefly described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 to Figure 2 Various aspects of an exemplary 5G network architecture are shown.

[0036] Figure 3 A signaling diagram showing the network process for authorizing and authenticating AI / ML model transmission.

[0037] Figure 4A signaling diagram illustrating the processes involving NWDAF (AnLF), NRF, NWDAF (MTLF) and ADRF according to some embodiments of the present disclosure is shown.

[0038] Figure 5 (which includes FIG. 5A to FIG. 5B ) shows a signaling diagram of another process involving NWDAF (AnLF), NRF, NWDAF (MTLF) and ADRF according to other embodiments of the present disclosure.

[0039] Figure 6 Exemplary methods (eg, processes) for a consumer NF of a communication network according to various embodiments of the present disclosure are illustrated.

[0040] Figure 7 Exemplary methods (eg, processes) for a producer NF of a communication network according to various embodiments of the present disclosure are illustrated.

[0041] Figure 8 Exemplary methods (eg, processes) for an NRF of a communication network according to various embodiments of the present disclosure are illustrated.

[0042] Fig. 9 Exemplary methods (eg, processes) for ADRF of a communication network according to various embodiments of the present disclosure are illustrated.

[0043] Fig.10 A communication system according to various embodiments of the present disclosure is shown.

[0044] Fig.11 A UE according to various embodiments of the present disclosure is shown.

[0045] Fig.12 A network node according to various embodiments of the present disclosure is shown.

[0046] Fig.13 A host computing system according to various embodiments of the present disclosure is shown.

[0047] Fig.14 is a block diagram of a virtualization environment that may virtualize functionality implemented by some embodiments of the present disclosure.

[0048] Fig.15 Communications between a host computing system, a network node, and a UE via multiple connections are illustrated according to various embodiments of the present disclosure. DETAILED DESCRIPTION

[0049] The embodiments briefly summarized above will now be described more fully with reference to the accompanying drawings. These descriptions are provided by way of example to illustrate the subject matter to those skilled in the art and should not be construed as limiting the scope of the subject matter to only the embodiments described herein. More specifically, examples illustrating the operation of various embodiments according to the above advantages are provided below.

[0050] In general, unless clearly defined and / or implied different meanings in the context of use, all terms used herein are interpreted according to their usual meaning for those of ordinary skill in the relevant technical field. Unless otherwise clearly stated or clearly implied from the context of use, all references to "one / an / element, equipment, component, device, step, etc." should be openly interpreted as referring to at least one instance in an element, equipment, component, device, step, etc. Unless an operation must be clearly described as after or before another operation and / or an operation must be after or before another operation implicitly, the operation of any method and / or process disclosed herein does not have to be performed in the exact order disclosed. Where appropriate, any feature of any embodiment disclosed herein may be applicable to any other disclosed embodiment. Similarly, where appropriate, any advantage of any embodiment described herein may be applicable to any other disclosed embodiment.

[0051] Furthermore, the following terms are used throughout the description given below:

[0052] - Radio access node: As used herein, a "radio access node" (or equivalently, a "radio network node", "radio access network node" or "RAN node") may be any node in a radio access network (RAN) for wirelessly sending and / or receiving signals. Some examples of radio access nodes include, but are not limited to, base stations (e.g., gNBs in 3GPP 5G / NR networks or enhanced NodeBs or eNBs in 3GPP LTE networks), base station distributed components (e.g., CUs and DUs), high power or macro base stations, low power base stations (e.g., micro, pico, femto or home base stations, etc.), integrated access backhaul (IAB) nodes, transmission points (TPs), transmission reception points (TRPs), remote radio units (RRUs or RRHs), and relay nodes.

[0053] - Core network node: As used herein, a "core network node" is any type of node in the core network. Some examples of core network nodes include, for example, mobility management entity (MME), serving gateway (SGW), PDN gateway (P-GW), policy and charging rules function (PCRF), access and mobility management function (AMF), session management function (SMF), user plane function (UPF), charging function (CHF), policy control function (PCF), authentication server function (AUSF), location management function (LMF), etc.

[0054] - Wireless Device: As used herein, a "wireless device" (or "WD" for short) is any type of device capable of, configured, arranged and / or operable to wirelessly communicate with a network node and / or other wireless devices. Wireless communication may include sending and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves and / or other types of signals suitable for sending information through the air. Unless otherwise specified, the term "wireless device" is used interchangeably herein with the term "user equipment" ("UE" for short), both terms having different meanings than the term "network node".

[0055] - Radio node: As used herein, a "radio node" may be a "radio access node" (or equivalent terms) or a "wireless device".

[0056] - Network node: As used herein, a "network node" is any node that is part of a radio access network (e.g., a radio access node or equivalent term) or part of a core network (e.g., the core network node discussed above) of a cellular communication network. Functionally, a network node is a device that is capable, configured, arranged and / or operable to communicate directly or indirectly with a wireless device and / or with other network nodes or devices in a cellular communication network to enable and / or provide radio access to a wireless device, and / or perform other functions (e.g., management) in a cellular communication network.

[0057] - Node: As used herein, the term "node" (without a prefix) may be any type of node capable of operating in or with a wireless network (including a RAN and / or a core network), including a radio access node (or equivalent terms), a core network node, or a wireless device. However, the term "node" may be limited to a specific type (e.g., a radio access node, an IAB node) based on its specific characteristics in any given context.

[0058] The above definitions are not meant to be exclusive. In other words, various terms in the above terms may be explained and / or described using the same or similar terms elsewhere in this disclosure. However, if other explanations and / or descriptions conflict with the above definitions, the above definitions shall prevail.

[0059] Note that the description given herein focuses on 3GPP cellular communication systems, and therefore often uses 3GPP LTE terminology or terminology similar to 3GPP LTE terminology. However, the concepts disclosed herein are not limited to 3GPP systems, and may be applied to any communication system that can benefit from it. Furthermore, although the term "cell" is used herein, it should be understood that (particularly for 5G NR) beams may be used instead of cells, and therefore the concepts described herein are equally applicable to both cells and beams.

[0060] Figure 2 An exemplary architecture of a 5GC (200) with service-based interfaces and various 3GPP-defined NFs within the control plane (CP) is shown. This includes the following items:

[0061] - Application Function (AF, with Naf interface), interacts with 5GC to provide information to the network operator and subscribe to specific events occurring in the operator's network. AF provides applications with services delivered in a layer different from the layer where the service has been requested (i.e., the signaling layer), controlling flow resources according to what has been negotiated with the network. AF delivers dynamic session information to PCF (via N5 interface), including a description of the media to be delivered by the transport layer.

[0062] - Policy Control Function (PCF, with Npcf interface), supports a unified policy framework to manage network behavior by: Providing PCC rules (e.g., regarding the handling of each service data flow under PCC control) to SMF via N7 reference point. PCF provides policy control decisions and flow-based charging control (including service data flow detection, gating, QoS) and flow-based charging (in addition to credit management) to SMF. PCF receives session and media related information from AF and notifies AF of service (or user) plane events.

[0063] - User Plane Function (UPF) - supports the processing of user plane traffic based on rules received from the SMF, including packet inspection and different execution actions (e.g., event detection and reporting). The UPF communicates with the RAN (e.g., NG-RNA) via the N3 reference point, with the SMF (discussed below) via the N4 reference point, and with external packet data networks (PDNs) via the N6 reference point. The N9 reference point is used for communication between two UPFs.

[0064] - Session Management Function (SMF, with Nsmf interface), interacts with the decoupled service (or user) plane, including creating, updating, and deleting protocol data unit (PDU) sessions and managing session context with the user plane function (UPF), such as for event reporting. For example, SMF performs data flow detection (based on filter definitions included in PCC rules), online and offline charging interactions, and policy enforcement.

[0065] - Charging Function (CHF, with Nchf interface), responsible for merging online and offline charging functions. The charging function provides quota management (for online charging), re-authorization triggers, rating conditions, etc., and is informed about usage reports from the SMF. Quota management involves granting a specific number of units (e.g., bytes, seconds) for a service. The CHF also interacts with the charging system.

[0066] - Access and mobility management function (AMF, with Namf interface) terminates the RAN CP interface and handles all mobility and connection management of the UE (similar to the MME in EPC). The AMF communicates with the UE via the N1 reference point and with the RAN (e.g., NG-RAN) via the N2 reference point.

[0067] - Network Exposure Function (NEF) with Nnef interface - acts as an entry point into the operator network by securely exposing the capabilities and events of 5GC NFs to AFs inside and outside the 5GC, and by providing a way for AFs to securely provide information to the 3GPP network. For example, NEF provides services that allow AFs to provide specific subscription data (e.g., expected UE behavior) for various UEs.

[0068] - Network Repository Function (NRF, 220) with Nnrf interface – provides service registration and discovery, enabling NFs to identify appropriate services available from other NFs.

[0069] - Network Slice Selection Function (NSSF) with Nnssf interface - A "network slice" is a logical partition of a 5G network that provides specific network capabilities and features, e.g., to support a specific service. A network slice instance is a collection of NF instances and the network resources (e.g., compute, storage, communication) required to provide the capabilities and features of a network slice. NSSF enables other NFs (e.g., AMF) to identify a network slice instance that is suitable for the service desired by the UE.

[0070] - Authentication Server Function (AUSF) with Nausf interface – Based on the user’s home network (HPLMN), it performs user authentication and computes security key material for various purposes.

[0071] - Network Data Analysis Function (NWDAF, 210) with Nnwdaf interface, described in more detail above and below.

[0072] - Location Management Function (LMF) with Nlmf interface – supports various functions related to the determination of the UE location, including location determination of the UE and obtaining any of the following: DL location measurements or position estimates from the UE; UL location measurements from the NG RAN; and non-UE associated assistance data from the NG RAN.

[0073] The Unified Data Management (UDM) function supports the generation of 3GPP authentication credentials, user identity handling, access authorization based on subscription data, and other subscriber-related functions. To provide this functionality, UDM uses subscription data (including authentication data) stored in the 5GC Unified Data Repository (UDR). In addition to interacting with UDM, UDR also supports the storage and retrieval of policy data by PCF and the storage and retrieval of application data by NEF. The Data Storage Function (DSF) allows each NF to store its own context.

[0074] The communication link between the UE and the 5G network (AN and CN) can be grouped into two different layers. The UE communicates with the CN through the non-access stratum (NAS) and communicates with the AN through the access stratum (AS). All NAS communications are carried out via the NAS protocol ( Figure 2 The N1 interface in the UE layer is between the UE and the AMF. The security of the communication on these layers is provided by the NAS protocol (for NAS) and the PDCP protocol (for AS).

[0075] 3GPP Release 17 enhances SBA by adding a data management framework, which includes the Data Collection Coordination Function (DCCF) and the Messaging Framework Adapter Function (MFAF) defined in detail in 3GPP TR23.700-91 (v17.0.0). As mentioned above, the data management framework is backward compatible with the NWDAF functions of Release 16. For Release 17, the baseline of the services provided by the DCCF (e.g., to the NWDAF) are the NF services of Release 16 for obtaining data. For example, the baseline of the DCCF service used by the NWDAF consumer to obtain UE mobility data is Namf_EventExposure.

[0076] 3GPP TS 23.288 (v17.2.0) specifies that NWDAF is the primary network function for computing analytics reports. The 5G system architecture allows any NF to use the DCCF function and associated Ndccf services to obtain analytics from NWDAF. NWDAF can also store and retrieve analytics information from the Analytics Data Repository Function (ADRF).

[0077] 3GPP TS 23.288 also classifies NWDAF into two sub-functions (or logical functions): NWDAF analysis logic function (NWDAF AnLF), which performs the analysis process; and NWDAF model training logic function (NWDAF MTLF), which performs training and retraining of the ML model used by NWDAF AnLF. In the following, the terms "AnLF", "NWDAF AnLF" and "NWDAF (AnLF)" will be used interchangeably. Similarly, the terms "MTLF", "NWDAF MTLF" and "NWDAF (MTLF)" will be used interchangeably.

[0078] 3GPP TS 23.288 (v17.2.0) specifies a subscription / notification procedure for consumer NFs to retrieve ML models associated with one or more analysis IDs whenever a new ML model has been trained by the NWDAF MTLF and becomes available. This is called ML model provisioning and is implemented by the Nnwdaf_MLModelProvision service.

[0079] 3GPP TR 33.738 (v0.2.0) describes a study of security aspects of enablers for network automation for 5G. One of the objectives of the study is the security of AI / ML model sharing and storage, which is identified as "Key Issue #3". The following text from 3GPP TR 33.378 describes various aspects of this issue. In this article, "NFc" refers to the consumer NF, and "NFp" refers to the producer NF, from the perspective of the AI / ML of interest.

[0080] ***BEGIN 3GPP TEXT***

[0081] 5.3.1 Problem Details

[0082] The AI / ML model is shared between NWDAF and / or NF (i.e., NWDAF to NWDAF, ADRF to NWDAF, etc.). In different scenarios, the NF producer of the AI / ML model can store the model in ADRF, NWDAF, or other entities.

[0083] ADRF (Analytical Data Repository Function) is being enhanced to store AI / ML models, thereby facilitating the distribution and sharing of these models among NFs. Since AI / ML models and their algorithms are usually proprietary (i.e., protected by the designer's intellectual property), it is necessary to ensure that only NFs that have been provided with access authorization to the AI / ML models can read and use these models. In addition, ADRF itself cannot be considered a fully trusted entity for storing sensitive AI / ML data models. These models are indeed exposed at rest in ADRF.

[0084] The current authorization scheme defined by 3GPP for SBA is only applicable to service level or resource / operation level scope. This authorization granularity may not be enough in AI / ML model sharing scenarios because ADRF (Analysis Data Repository Function) or NWDAF, or any other network function capable of storing AI / ML models, cannot verify whether the NF consumer is authorized to retrieve the AI / ML model.

[0085] 5.3.2 Security Threats

[0086] An unauthorized NFc (which in principle has no right to retrieve a specific model stored by NFp) may have access to the storage entity and retrieve the model.

[0087] If no protection exists against accessing and reading AI / ML models in ADRF stored by NFp, a compromised ADRF could expose algorithms and sensitive data to unauthorized entities who could easily abuse it and / or further distribute it to other entities, creating a larger data security vulnerability.

[0088] 5.3.3 Potential security requirements

[0089] AI / ML models should be protected between the entity that produces the ML model or stores the ML model in ADRF (e.g., NWDAF, NFp containing MTLF) and the entity that consumes the model (NFc).

[0090] ADRF (Analytical Data Repository Function) or any other network function capable of storing an AI / ML model should be able to authorize NFc to retrieve that AI / ML model.

[0091] NF service consumers should be authorized to access AI / ML models in ADRF (or any other NF capable of storing ML models, such as NWDAFMTLF).

[0092] ***END 3GPP TEXT***

[0093] 3GPP TR 33.738 (v0.2.0) also describes a solution for authorizing and authenticating AI / ML model transmission, which is identified as "Solution #2". This security solution protects the AI / ML model between the first entity (e.g., NF) that generates the AI / ML model (or stores the AI / ML model in ADRF) and the second entity (NFc) that consumes the model. In this solution, ADRF uses the authorization token to verify whether NFc is allowed to access the ML model.

[0094] Figure 3 The signaling diagram of this solution for authorizing and authenticating AI / ML model transmission is shown. Figure 3 As shown, the signaling is between NWDAF (AnLF) / NFc, the authorization server (eg, NRF), NWDAF (MTLF) / NFp, and ADRF. Figure 3 The operations shown are given numerical labels, but this is intended to facilitate explanation and is not intended to require or imply any particular order of operations unless otherwise noted below.

[0095] In operation 1, MTLF trains the ML model and sends the ML model to ADRF by calling the Nadrf_DataManagement_StorageRequest(ML model) service operation. In addition to the model metadata, the message may include the ML model ID, analysis ID, vendor ID, MAC or SHA256 signature of the application binary, the environment required to execute the ML model, URL / link for retrieving configuration, and secret / signing key / certificate for generating authentication credentials. MTLF may send the ML model encrypted using a symmetric key (e.g., AES key) before storage.

[0096] In operation 2, the ADRF stores the ML model and response as specified in 3GPP TS 23.288 (v17.6.0), except that the storage is performed by the ADRF. In operation 3, the NFc (e.g., NWDAF AnLF) contacts the NRF and requests an access token using the existing procedures specified in 3GPP TS33.501 (v17.7.0). In operation 4, the NRF sends the access token along with the MTLF ID using the existing procedures specified in 3GPP TS 23.288.

[0097] In operation 5, NWDAF (AnLF) requests the ML model ID from NWDAF (MTLF) using the Nnwdaf_MLModelProvision service operation and the access token, which NWDAF (MTLF) retrieves based on the ML analysis ID and / or ADRF ID. NWDAF (MTLF) also verifies the received access token. In operation 6, NWDAF (MTLF) sends a Nnwdaf_MLModelProvision response, which includes the encryption key used to encrypt the AI / ML model in operation 1. In addition, NWDAF (MTLF) may include a one-time credential for accessing the model from ADRF, including any of the following:

[0098] - a random number, which was shared as part of the metadata in operation 1;

[0099] - a MAC or hash value of the binary or random number shared as part of the data in operation 1;

[0100] - a signing key that is the private key of MTLF, of which the public part was shared in operation 1;

[0101] - A credential generated by MTLF’s signing key, such as a JWT token or certificate.

[0102] One-time credentials can be used to limit the number of accesses from the NFc. Even so, a "one-time" credential can also be used as a regular authorization token for accessing the ML model multiple times, i.e., not just once (as the name implies).

[0103] In operation 7, the NWDAF (AnLF) uses the ADRF service procedure to request the ML model, including the one-time credential received in operation 6. In operation 8, the ADRF verifies the one-time credential, and if the verification is successful, provides the stored AI / ML model to the NWDAF (AnLF).

[0104] As mentioned above, AI / ML models are generally considered important intellectual property of their owners (e.g., 5GC vendors), and therefore their confidentiality and integrity need to be protected at all times. 3GPP is studying the feasibility of sharing or storing ML models in network equipment that may be provided by different vendors. Under this arrangement, AI / ML models should be protected from being accessed and used by consumer NFs provided by a different vendor than the AI / ML model. However, no specified solution for this requirement currently exists. For example, Figure 3 The solutions shown do not provide the required security in a multi-vendor network environment.

[0105] Embodiments of the present disclosure address these and other issues, problems, and / or difficulties by providing secure AI / ML model sharing between NFp (e.g., NWDAF MTLF) and NFc (e.g., NWDAF AnLF), and AI / ML model storage in ADRF. For example, NFp (e.g., NWDAF MTLF) may authorize the transfer and storage of its AI / ML models in an external repository (e.g., ADRF), and / or the retrieval of its AI / ML models from the repository. As another example, NFp (e.g., NWDAF MTLF) may authorize the transfer of its AI / ML models to NFc (e.g., NWDAF AnLF). As another example, NFp (e.g., NWDAF MTLF) may confidentially protect its AI / ML models and / or model location information during the above-mentioned transfer scenarios.

[0106] Embodiments of the present disclosure may provide various benefits and / or advantages. By providing the owner / producer of the AI / ML model with the ability to protect the AI / ML model during various transmission, storage, and retrieval scenarios, the embodiments improve the security of confidential and / or sensitive AI / ML models, thereby facilitating the deployment of such models in multi-vendor communication networks (e.g., 5GC).

[0107] In the following description of various embodiments, the terms NFp and NWDAF (MTLF) may be used interchangeably, and the terms NFc and NWDAF (AnLF) may be used interchangeably. Similarly, the terms “model”, “ML model” and “AI / ML model” may be used interchangeably.

[0108] Figure 4 A signaling diagram is shown of the processes involving NWDAF (AnLF) (410), NRF (420), NWDAF (MTLF) (430) and ADRF (440) according to some embodiments of the present disclosure. Figure 4 The operations shown are given numerical labels, but this is intended to facilitate explanation and is not intended to require or imply any particular order of operations unless otherwise noted below.

[0109] In operation 0, the NWDAF (MTLF) trains the ML model and can encrypt it and protect its integrity. The key used for protection can be referenced by a key ID or a certificate and is bound to the interoperability ID, ML model ID, analysis ID, vendor ID, etc. It is assumed that the NF authorized for the same interoperability ID, ML model ID, analysis ID, vendor ID, etc. is provided with the corresponding key for encryption / decryption / verification.

[0110] In addition, NWDAF (MTLF) registers its NF profile in NRF using ML model information, which can include analysis ID, interoperability ID, vendor ID, ML model filter, model URL, model ID, and model authorization information. The model authorization information specifies the scope of authorization for accessing the ML model, such as requester, provider, model owner, and target model information. As a more specific example:

[0111] - The target model is identified by: Interoperability ID, Vendor ID, Analysis ID, Model Owner, Model Filter, Model URL, Model ID; and

[0112] - The scope is identified by: allowed requestor and / or provider NF types / IDs, allowed requestor and / or provider vendor IDs, allowed interoperability IDs, etc.

[0113] In operation 1, NWDAF (MTLF) sends the trained ML model to ADRF for storage by calling the Nadrf_DataManagement_StorageRequest service operation. NWDAF (MTLF) includes in the message the SBA token, the encrypted ML model, one or more model identifiers (e.g., ML model ID, analysis ID, vendor ID, etc.), and optional model authorization information to facilitate subsequent access to the model. In operation 2, ADRF stores the encrypted ML model and responds with a URL corresponding to the storage location of the ML model file (i.e., in ADRF). In operation 3, NWDAF (MTLF) updates its NF configuration file in NRF using the ML model information received from ADRF (e.g., URL).

[0114] In some variants, ADRF may register the model authorization information in its own NF profile in NRF (similar context to operation 9). In other variants, NWDAF (MTLF) may register the model authorization information in the NF profile of ADRF (i.e., on behalf of ADRF) in NRF (similar context to operation 0).

[0115] In operation 4, the NWDAF (AnLF) discovers the NWDAF (MTLF) using the existing procedures specified in 3GPP TS 23.288. In operation 4a, the NWDAF (AnLF) contacts the NRF to request an access token ("Token 1") using the existing procedures specified in 3GPP TS 33.501. In operation 4b, the NRF provides Token 1 to the NWDAF (AnLF) according to these procedures.

[0116] In operation 5, NWDAF (AnLF) retrieves the ML model using the Nnwdaf_MLModelProvision or Nnwdaf_MLModelInfo_Request service operation and the access token (Token 1) received in operation 4. If NWDAF (MTLF) stores the model locally, it will perform operation 8 described below. If the model is stored in ADRF, operations 6 to 7 are performed after operation 5.

[0117] In operation 6a, the NWDAF (MTLF) requests a token for accessing the ML model from the NRF by providing the analysis ID, interoperability ID, ML model ID, model owner information (e.g., MTLF ID), etc. In operation 6b, the NRF verifies whether the NWDAF (MTLF) has the right to access the requested ML model based on the model authorization information previously registered in the NRF (e.g., operations 0, 3), and if so, generates a second access token ("Token 2") and sends it to the NWDAF (MTLF).

[0118] In operation 7a, NWDAF (MTLF) requests the ML model from ADRF using the Nadrf_Model_Request service operation and includes the analysis ID, interoperability ID, ML model ID, and token 2. In operation 7b, ADRF verifies whether NWDAF (MTLF) has the authority to retrieve the ML model based on the received token 2 or the ML model authorization information received in operation 1. If verified, in operation 7c, ADRF sends the encrypted ML model to NWDAF (MTLF).

[0119] In operation 8, the NWDAF (MTLF) sends the ML model to the NWDAF (AnLF) using the Nnwdaf_MLModelProvision response according to the service for the request in operation 5. In this operation, the ML model may still be encrypted (such as the encrypted ML model received in operation 7c) or may be decrypted by the NWDAF (MTLF) and sent in plain text. If the ML model is sent in encrypted form, the NWDAF (MTLF) may include information that helps the NWDAF (AnLF) locate the key for decryption / verification (e.g., an ID, certificate, or certificate URL associated with the key used to protect the ML model).

[0120] In some variations, the ML model information is presented in the form of Figure 4 The method shown in FIG. 1 is similar to that shown in FIG. 1 , but different services, messages and / or protocols are used. The signaling flow of these embodiments is similar to that of FIG. 1 . Figure 4The signaling flows shown are the same, but other download services, messages and / or protocols may be used in operations 1b / 1c and 6 to 9. For example, the ML model may be obtained via a URL via an unspecified process that is assumed to be vendor implementation specific.

[0121] Figure 5 (included FIG. 5A to FIG. 5B ) shows a signaling diagram of another process involving NWDAF (AnLF) (510), NRF (520), NWDAF (MTLF) (530) and ADRF (540) according to other embodiments of the present disclosure. Although the operations shown in Figure 5 are given numerical labels, this is intended to facilitate explanation and does not require or imply any particular order of operations, unless otherwise specified below.

[0122] Operations 0a to 0b are the same as above Figure 4 In operation 1a, NWDAF (MTLF) is not Figure 4 Instead of sending an encrypted ML model as in operation 1, the ADRF sends a URL ("URL1") where the ML model is stored and from which the model can be obtained. In operation 1b, the ADRF uses the Nmtlf_Model_Request service operation to send URL1, the ML model ID, and an access token ("Token2") to obtain / get the ML model. In some variants, the ADRF may also include an analysis ID and / or an interoperability ID. In operation 1c, the NWDAF (MTLF) provides the ML model in the response based on validating the access token.

[0123] In different variations of operation 1a, URL1 may be sent in plain text or encrypted form. In the case where URL1 is encrypted, NWDAF (MTLF) may include in the message information to assist ADRF in locating a key for decryption / verification (e.g., an ID, certificate, or certificate URL associated with the key used to protect URL1).

[0124] Although not shown in FIG5 , ADRF may obtain Token 2 from NRF in a manner similar to the manner in which NWDAF (AnLF) obtains Token 1 from NRF in operations 6a to 6b described below. When issuing Token 2, NRF checks whether ADRF can obtain the ML model from the URL based on the model authorization information registered in operation 0.

[0125] Operations 2 to 5a are the same as above Figure 4The corresponding operations in are the same. In operation 5b, the NWDAF (MTLF) sends the address of the ML model to the NWDAF (AnLF) using the Nnwdaf_MLModelProvision response service operation. For example, the address may be URL1 corresponding to the encrypted model stored in the NWDAF (MTLF), or URL2 corresponding to the encrypted model stored in the ADRF. In some variants, the NWDAF (MTLF) may include information for helping the NWDAF (AnLF) locate the key for decrypting / verifying the ML model (e.g., an ID, certificate, or certificate URL associated with the key used to protect the ML model).

[0126] In different variations of operation 5b, URL1 / URL2 may be sent in plain text or encrypted form. In the case where URL1 / URL2 is encrypted, NWDAF (MTLF) may include in the message information to help NWDAF (AnLF) locate the key for decryption / verification (e.g., an ID, certificate, or certificate URL associated with the key used to protect URL1 / URL2).

[0127] In operation 6a, the NWDAF (AnLF) requests a token for accessing the ML model via a URL (e.g., a download service) from the NRF. The request includes the analysis ID, interoperability ID, model owner information (e.g., MTLFID), URL1 or ULR2 received in operation 5b. In operation 6b, the NRF verifies whether the NWDAF (AnLF) is authorized to access the ML model according to the model authorization information registered in the NRF (e.g., operation 0a, 3), and issues Token 2.

[0128] If URL1 is received in operation 5b, operations 7a, 8a, and 9a are performed. In operation 7a, the NWDAF (AnLF) invokes the Nmtlf_Model_Download service operation to download the ML model from the NWDAF (MTLF) provided with the analysis ID, interoperability ID, URL1, and token 2. In operation 8a, the NWDAF (MTLF) verifies whether the NWDAF (AnLF) is authorized to retrieve the ML model based on the received token 2 or local ML model authorization. In operation 9a, based on the verification, the NWDAF (MTLF) Figure 4 The encrypted ML model is sent to NWDAF (MTLF) in a similar manner to operation 8 in FIG.

[0129] If URL2 is received in operation 5b, operations 7b, 8b, and 9b are performed. In operation 7b, NWDAF (AnLF) invokes the Nadrf_Model_Download service operation to download the ML model from ADRF, providing the analysis ID, interoperability ID, URL2, and token2. In operation 8b, NWDAF (AnLF) is authorized to retrieve the ML model based on the received token2 or the model authorization information received in operation 1. In operation 9b, based on this verification, ADRF Figure 4 The encrypted ML model is sent to NWDAF (MTLF) in a similar manner to operation 8 in FIG.

[0130] In some variations, a different protocol (eg, FTP) may be used instead of any of the service-based interfaces (SBIs) used in operations 1b / 1c, 7a / 7c, and 9a / 9c of FIG. 5 .

[0131] In some variants, instead of discovering NWDAF (MTLF) in operation 4, NWDAF (AnLF) discovers ADRF via NRF based on interoperability ID, ML model ID, analysis ID, vendor ID, etc. In this case, NWDAF (AnLF) can obtain the address (e.g., URL or FQDN) of the ML model directly from ADRF.

[0132] For example, in operations 4a to 4b, the NWDAF (AnLF) requests and receives an SBA token (Token 1) for accessing the ADRF. In this case, operations 5a to 5b are not performed, and in operations 6a to 6b, the NWDAF (AnLF) requests and receives an access token (Token 2) for downloading the ML model from the ADRF, for example, via URL 2. Note that in these variants, Token 2 may be the same as Token 1. In addition, URL 2 may be provided in plain text or encrypted form in operation 6b in a manner similar to that described above.

[0133] exist Figure 4 In some variations of the embodiment shown in FIG. 5 , if the NWDAF (AnLF) wants to receive updates to the ML model through the NWDAF (MTLF), the NWDAF (AnLF) subscribes to model updates based on the interoperability ID, ML model ID, analysis ID, vendor ID, etc. If the model is updated, the NWDAF (MTLF) may encrypt and integrity protect the updated model using a different key than that used for the previous model version. In the event that the updated model is retrieved from the NWDAF (MTLF), the entity may provide information (e.g., an ID, certificate, or certificate URL associated with the key used to protect the updated model) for locating the key for decryption / verification.

[0134] Alternatively, if the NWDAF (AnLF) retrieves the updated model from the ADRF, the NFp notifies the NWDAF (AnLF) of the model ID, URL and / or FQDN, optionally including the new key ID, etc., in a manner similar to operation 5b discussed above. The NWDAF (AnLF) obtains the encrypted updated ML model from the ADRF and performs decryption and integrity checking using the new key identified by the NWDAF (MTLF).

[0135] Although the embodiments have been described above in the specific context of NWDAF and its logical functions MTLF and AnLF, the skilled person will understand that the basic principles of the above embodiments are equally applicable to other NFs, logical functions, nodes, etc. that may be called by different names but perform similar operations as MTLF and AnLF.

[0136] You can refer to Figures 6 to 9 To further illustrate the above embodiments, Figures 6 to 9 Exemplary methods (eg, processes) for a consumer NF, a producer NF, an NRF, and an ADRF are depicted, respectively. In other words, various features of the operations described below correspond to the various embodiments described above. Figures 6 to 9 The exemplary methods shown can be used in conjunction (e.g., in conjunction with each other and with other processes described herein) to provide the benefits, advantages, and / or solutions to the problems described herein. Fig. 9 These exemplary methods are shown in the figures by specific blocks in a specific order, but the operations corresponding to the blocks can be performed in an order different from the order shown, and can be combined and / or divided into blocks and / or operations with functions different from those shown. Optional blocks and / or operations are indicated by dotted lines.

[0137] More specifically, Figure 6 An exemplary method (e.g., process) for a consumer NF (NFc) of a communication network (e.g., 5GC) according to various embodiments of the present disclosure is shown. As described elsewhere herein, Figure 6 The exemplary method shown in may be performed by a NFc (eg, a NWDAF (AnLF) or a network node hosting a NWDAF (AnLF)).

[0138] The exemplary method may include the operation of block 610, wherein the NFc may send a first request for a first access token associated with an ML model to a first NF of the communication network. The first request includes one or more of the following items associated with the ML model: an analysis ID and an interoperability ID. The exemplary method may also include the operation of block 620, wherein the NFc may receive a first response including the first access token from the first NF. The exemplary method may also include the operation of block 630, wherein the NFc may send a second request for the ML model to a producer NF (NFp) of the communication network. The second request includes at least one of the analysis ID and the interoperability ID and the first access token. The exemplary method may also include the operation of block 640, wherein the NFc may receive a second response from the NFp, the second response including one or more of the following items: the ML model, an identifier of the ML model, and an address of a storage resource associated with the second NF of the communication network, from which the ML model may be obtained.

[0139] In some embodiments, the first NF is a Network Repository Function (NRF). In other embodiments, the first NF is an Analysis Data Repository Function (ADRF). In some embodiments, one or more of the following apply:

[0140] -NFc is the analysis logic function of the network data analysis function NWDAF (AnLF); and

[0141] - NFp is the model training logic function NWDAF (MTLF) of the network data analysis function.

[0142] In some embodiments, the second response includes an ML model (e.g., Figure 4 As shown in FIG. 1 ), the ML model may be encrypted. In this case, the second response also includes information that can be used to locate a key that can be used to decrypt and verify the ML model. Some examples of such information are discussed above.

[0143] In other embodiments, the second response includes an address of a storage resource associated with the ML model (e.g., as shown in FIG. 5 ), and the exemplary method further includes the following operations labeled with corresponding box numbers:

[0144] - (650) sending a third request for a second access token associated with the ML model to the first NF, wherein the third request includes at least one of the analysis ID and the interoperability ID and an address of a storage resource associated with the second NF;

[0145] - (660) receiving a third response including a second access token from the first NF; and

[0146] - (670) Obtain the ML model from the second NF using the second access token and the address of the storage resource associated with the second NF.

[0147] In some of these embodiments, the address of the storage resource is encrypted, and the second response also includes information that can be used to locate a key that can be used to decrypt and verify the storage resource address.

[0148] In some embodiments, the address of the storage resource associated with the second NF is a universal resource locator (URL). In other embodiments, the address of the storage resource associated with the second NF is a fully qualified domain name (FQDN). In some embodiments, the second NF is NFp. In other embodiments, the second NF is ADRF.

[0149] also, Figure 7 An exemplary method (e.g., process) of NFp for a communication network (e.g., 5GC) according to various embodiments of the present disclosure is shown. As described elsewhere herein, Figure 7 The exemplary method shown in may be performed by a NFp (eg, a NWDAF (MTLF) or a network node hosting a NWDAF (MTLF)).

[0150] The exemplary method includes an operation of box 710, in which the NFp may register information associated with an ML model in an NRF of a communication network. The ML model is generated, owned and / or maintained by the NFp, where "and / or" represents any one or more of the three listed properties. The registration information associated with the ML model includes an analysis ID and an interoperability ID. The exemplary method also includes an operation of box 720, in which the NFp may encrypt the ML model and send a first request for storing the encrypted ML model to the ADRF of the communication network. The first request includes a first address of the encrypted ML model or a storage resource associated with the NFp, from which the ML model may be obtained.

[0151] In some embodiments, the exemplary method may further include the operation of block 750, wherein the NFp may receive a second request for the ML model from the NFc of the communication network. The second request includes at least one of the analysis ID and the interoperability ID and the first access token. The exemplary method may further include the operation of block 780, wherein based on verifying the first access token, the NFp may send a second response to the NFc, the second response including one or more of the following items: the ML model; an identifier of the ML model; a first address of a storage resource associated with the NFp; and a second address of a storage resource associated with the ADRF, from which the ML model may be obtained.

[0152] In some of these embodiments, the first address of the storage resource associated with the NFp is a first universal resource locator (URL), and the second address of the storage resource associated with the ADRF is a second URL or fully qualified domain name (FQDN).

[0153] In some of these embodiments, the first request includes a first address of a storage resource associated with the NFp, and the second response includes one of:

[0154] - a first address of a storage resource associated with the NFp, or

[0155] - A second address of a storage resource associated with the ADRF.

[0156] Figure 5 shows examples of these embodiments.

[0157] In some variations of these embodiments, the first address included in the first request is encrypted, and the first request also includes information that can be used to locate a key that can be used to decrypt and verify the first address. In some variations of these embodiments, the first address or the second address included in the second response is encrypted, and the second response also includes information that can be used to locate a key that can be used to decrypt and verify the first address or the second address.

[0158] In some further variations, the exemplary method may also include the following operations marked with corresponding box numbers:

[0159] - (730) receiving another request for the ML model from the ADRF, wherein the another request includes a first address of a storage resource associated with the NFp and a second access token;

[0160] - (735) based on validating the second access token, sending another response including the encrypted ML model to the ADRF; and

[0161] - (740) Then receiving from the ADRF a second address of a storage resource associated with the ADRF.

[0162] In some further variations, the registration information associated with the ML model (e.g., from block 710) further includes a first address of a storage resource associated with the NFp, and the exemplary method further includes the operation of block 745, wherein the NFp may update the registration information associated with the ML model in the NRF to include the received second address.

[0163] In some variations of these embodiments, the second response includes a first address of a storage resource associated with the NFp, and the exemplary method further includes the following operations labeled with corresponding box numbers:

[0164] - (790) receiving a third request for the ML model from the NFc, wherein the third request includes: a third access token associated with the ML model, the first address, and at least one of the analysis ID and the interoperability ID; and

[0165] - (795) Based on verifying the third access token, send a third response including the ML model to the NFc.

[0166] In some further variations, the ML model in the third response is encrypted, and the third response also includes information that can be used to locate a key that can be used to decrypt and verify the ML model.

[0167] In other of these embodiments, the exemplary method may also include the following operations labeled with corresponding box numbers:

[0168] - (755) sending a fourth request for an access token associated with the ML model to the NRF of the communication network, wherein the fourth request includes at least one of the analysis ID and the interoperability ID;

[0169] - (760) receiving the requested access token from the NRF;

[0170] - (765) Sending a fifth request for the ML model to the ADRF, wherein the fifth request includes at least one of the analysis ID and the interoperability ID and the received access token;

[0171] -(770) Receive a fifth response including the ML model from the ADRF.

[0172] The received ML model is then included in a second response sent to the NFc (e.g., in block 780).

[0173] In some embodiments, NFc is NWDAF (AnLF). In some embodiments, NFp is NWDAF (MTLF).

[0174] also, Figure 8 An exemplary method (e.g., process) for NRF of a communication network (e.g., 5GC) according to various embodiments of the present disclosure is shown. As described elsewhere herein, Figure 8 The exemplary method shown in may be performed by an NRF or a network node hosting an NRF.

[0175] The exemplary method includes an operation of block 810, wherein the NRF may register information associated with an ML model produced, owned, and / or maintained by a producer network function (NFp) of a communication network, wherein "and / or" represents any one or more of the three listed properties. The registered information associated with the ML model includes an analysis ID and an interoperability ID. The exemplary method also includes an operation of box 830, wherein the NRF may receive a first request for a first access token associated with the ML model from a NFc of the communication network. The first request includes at least one of the analysis ID and the interoperability ID. The exemplary method also includes an operation of box 840, wherein the NRF may send a first response including the first access token to the NFc.

[0176] In some embodiments, the exemplary method further includes the operation of block 850, wherein the NRF may receive a second request for a second access token from a first NF of the communication network. The second request includes at least one of the analysis ID and the interoperability ID and one of the following:

[0177] - a first address of a storage resource associated with the NFp, from which the ML model can be obtained; or

[0178] - A second address of a storage resource associated with the ADRF of the communication network, from which the ML model is available.

[0179] The example method may also include the operation of block 860, where the NRF may send a second response including a second access token to the first NF.

[0180] In some of these embodiments, the first address of the storage resource associated with the NFp is a first URL, and the second address of the storage resource associated with the ADRF is a second URL or FQDN. In some of these embodiments, the first NF is a NFc (e.g., as shown in FIG. 5 ). In other of these embodiments, the first NF is a NFp (e.g., as shown in FIG. 5 ). Figure 4 as shown).

[0181] In some of these embodiments, the registration information associated with the ML model (e.g., in block 810) further includes a first address of a storage resource associated with the NFp, and the exemplary method further includes an operation of block 820, wherein the NRF may update the registration information to include a second identifier of the storage resource associated with the ADRF, e.g., based on a request by the NFp.

[0182] In some embodiments, NFc is NWDAF (AnLF). In some embodiments, NFp is NWDAF (MTLF).

[0183] also, Fig. 9 An exemplary method (e.g., process) for ADRF of a communication network (e.g., 5GC) according to various embodiments of the present disclosure is shown. As described elsewhere herein, Fig. 9 The exemplary method shown in may be performed by an ADRF or a network node hosting an ADRF.

[0184] The exemplary method includes an operation of block 910, wherein the ADRF may receive a first request for storing an encrypted ML model from a NFp of a communication network. The first request includes the encrypted ML model or a first address of a storage resource associated with the NFp, from which the encrypted ML model may be obtained. The exemplary method also includes an operation of block 940, wherein the ADRF may store the encrypted ML model in a storage resource associated with the ADRF. The exemplary method also includes an operation of block 950, wherein the ADRF may send a first response to the NFp, the first response including a second address of a storage resource associated with the ADRF.

[0185] In some embodiments, the first request includes a first address of a storage resource associated with the NFp, and the exemplary method further includes the operation of block 920, where the ADRF may send another request for the ML model to the NFp. The other request includes the first address and the second access token. The exemplary method also includes the operation of block 930, where the ADRF may receive another response from the NFp including the encrypted ML model. The encrypted model is then stored in a storage resource associated with the ADRF (e.g., in block 940). FIG. 5 shows an example of these embodiments.

[0186] In other embodiments, the exemplary method further includes the operation of block 960, wherein the ADRF may receive a second request for the ML model from the communicating first NF. The second request includes at least one of the analysis ID and the interoperability ID and a third access token. The exemplary method may also include the operation of block 970, wherein based on verifying the third access token, the ADRF may send a second response including the ML model to the first NF.

[0187] In some of these embodiments, the first NF is a NFp (e.g., Figure 4 ). In other of these embodiments, the first NF is a NFc of the communication network (e.g., as shown in FIG. 5 ). In some variants of these embodiments, the NFc is a NWDAF (AnLF) and / or the NFp is a NWDAF (MTLF).

[0188] In some of these embodiments, the ML model included in the second response (eg, in block 970 ) is encrypted, and the second response also includes information that can be used to locate a key that can be used to decrypt and verify the ML model.

[0189] In some embodiments, the first address of the storage resource associated with the NFp is a first URL, and the second address of the storage resource associated with the ADRF is a second URL or FQDN.

[0190] Although various embodiments are described above in terms of methods, techniques and / or processes, a person of ordinary skill in the art will readily appreciate that such methods, techniques and / or processes may be embodied in various combinations of hardware and software, communication devices, computing devices, control devices, apparatuses, non-transitory computer-readable media, computer program products, etc., in various systems.

[0191] Fig.10 An example of a communication system 1000 according to some embodiments is shown. In this example, the communication system 1000 includes a telecommunications network 1002, which includes an access network 1004 (e.g., RAN) and a core network 1006, which includes one or more core network nodes 1008. The access network 1004 includes one or more access network nodes, such as network nodes 1010a to 1010b (one or more of which may be generally referred to as network nodes 1010), or any other similar 3GPP access nodes or non-3GPP access points. The network node 1010 facilitates direct or indirect connection of UEs, such as connecting UEs 1012a to 1012d (one or more of which may be generally referred to as UEs 1012) to the core network 1006 through one or more wireless connections.

[0192] Example wireless communications via wireless connections include sending and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for transmitting information without using wiring, cables, or other material conductors. In addition, in different embodiments, the communication system 1000 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that can facilitate or participate in the communication of data and / or signals (whether via a wired connection or via a wireless connection). The communication system 1000 may include any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system, and / or be connected to any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system interface.

[0193] UE 1012 may be any of a variety of communication devices, including wireless devices that are arranged, configured and / or operable to wirelessly communicate with network node 1010 and other communication devices. Similarly, network node 1010 is arranged, capable, configured and / or operable to communicate directly or indirectly with UE 1012 and / or with other network nodes or devices in telecommunication network 1002 to enable and / or provide network access (such as wireless network access) and / or to perform other functions (such as management) in telecommunication network 1002.

[0194] In the depicted example, the core network 1006 connects the network node 1010 to one or more hosts (such as the host 1016). These connections may be direct connections or indirect connections via one or more intermediate networks or devices. In other examples, the network node may be directly coupled to the host. The core network 1006 includes one or more core network nodes (e.g., 1008) composed of hardware and software components. The features of these components may be substantially similar to those described with respect to the UE, the network node, and / or the host, so that their descriptions are generally applicable to the corresponding components of the core network node 1008. The example core network node includes the functions of one or more of the following items: a mobile switching center (MSC), a mobility management entity (MME), a home subscriber server (HSS), an access and mobility management function (AMF), a session management function (SMF), an authentication server function (AUSF), a subscription identifier cancellation function (SIDF), a unified data management (UDM), a security edge protection agent (SEPP), a network open function (NEF), and / or a user plane function (UPF).

[0195] The host 1016 may be owned or under the control of a service provider other than the operator or provider of the access network 1004 and / or the telecommunications network 1002, and may be operated by or on behalf of the service provider. The host 1016 may host a variety of applications to provide one or more services. Examples of such applications include real-time and pre-recorded audio / video content, data collection services (e.g., retrieving and compiling data about various environmental conditions detected by multiple UEs), analysis functions, social media, functions for controlling or otherwise interacting with remote devices, functions for alarm and monitoring centers, or any other such functions performed by a server.

[0196] As a whole, Fig.10The communication system 1000 implements the connection between UE, network node and host. In this sense, the communication system can be configured to operate according to predefined rules or procedures such as a specific standard, which includes but is not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE) and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standards (e.g., 6G); Wireless Local Area Network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard (WiFi); and / or any other suitable wireless communication standards, such as Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any Low Power Wide Area Network (LPWAN) standards such as LoRa and Sigfox.

[0197] In some examples, the telecommunication network 1002 is a cellular network implementing 3GPP standardized features. Therefore, the telecommunication network 1002 can support network slicing to provide different logical networks to different devices connected to the telecommunication network 1002. For example, the telecommunication network 1002 can provide ultra-reliable low-latency communication (URLLC) services to some UEs, while providing enhanced mobile broadband (eMBB) services to other UEs, and / or providing massive machine type communication (mMTC) / massive IoT services to yet other UEs.

[0198] In some examples, UE 1012 is configured to send and / or receive information without direct human interaction. For example, the UE may be designed to send information to the access network 1004 according to a predetermined plan when triggered by an internal or external event or in response to a request from the access network 1004. In addition, the UE may be configured to operate in a single RAT mode or a multi-RAT mode or a multi-standard mode. For example, the UE may operate using any one or a combination of Wi-Fi, NR (New Radio), and LTE, i.e., configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved UMTS Terrestrial Radio Access Network) New Radio-Dual Connectivity (EN-DC).

[0199] In this example, the central node 1014 communicates with the access network 1004 to facilitate indirect communication between one or more UEs (e.g., UE 1012c and / or 1012d) and a network node (e.g., network node 1010b). In some examples, the central node 1014 may be a controller, a router, a content source and an analyzer, or any other communication device described herein with respect to the UE. For example, the central node 1014 may be a broadband router that enables the UE to access the core network 1006. As another example, the central node 1014 may be a controller that sends commands or instructions to one or more actuators in the UE. The commands or instructions may be received from the UE, the network node 1010, or received through executable code, scripts, processes, or other instructions in the central node 1014. As another example, the central node 1014 may be a data collector that acts as a temporary storage device for UE data, and in some embodiments, analysis or other processing of the data may be performed. As another example, the central node 1014 may be a content source. For example, for a UE that is a VR headset, display, speaker, or other media delivery device, the central node 1014 can retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, and then the central node 1014 provides it directly to the UE after performing local processing and / or after adding additional local content. In yet another example, the central node 1014 acts as a proxy server or orchestrator for the UE, especially if one or more of the UEs are low-energy IoT devices.

[0200] The central node 1014 may have a continuous / persistent or intermittent connection to the network node 1010b. The central node 1014 may also allow different communication schemes and / or scheduling between the central node 1014 and the UE (e.g., UE 1012c and / or UE 1012d) and between the central node 1014 and the core network 1006. In other examples, the central node 1014 is connected to the core network 1006 and / or one or more UEs via a wired connection. In addition, the central node 1014 may be configured to be connected to an M2M service provider through the access network 1004, and / or to another UE through a direct connection. In some scenarios, the UE may establish a wireless connection with the network node 1010 while still being connected through the central node 1014 via a wired or wireless connection. In some embodiments, the central node 1014 may be a dedicated central node, that is, a central node whose main function is to route communications from the network node 1010b to the UE / route communications from the UE to the network node 1010b. In other embodiments, the central node 1014 may be a non-dedicated central node - ie, a device operable to route communications between UEs and the network node 1010b but additionally operable as a communications origin and / or endpoint for certain data channels.

[0201] Fig.11 A UE 1100 according to some embodiments is shown. Examples of UEs include, but are not limited to, smart phones, mobile phones, cellular phones, voice over IP (VoIP) phones, wireless local loop phones, desktop computers, personal digital assistants (PDAs), wireless cameras, game consoles or devices, music storage devices, playback devices, wearable terminal devices, wireless endpoints, mobile stations, tablet computers, laptop computers, laptop embedded equipment (LEE), laptop-mounted equipment (LME), smart devices, wireless client equipment (CPE), vehicle-mounted or vehicle-embedded / integrated wireless devices, etc. Other examples include any UE identified by 3GPP, including narrowband Internet of Things (NB-IoT) UEs, machine type communication (MTC) UEs, and / or enhanced MTC (eMTC) UEs.

[0202] The UE may, for example, support device-to-device (D2D) communications by implementing 3GPP standards for sidelink communications, dedicated short-range communications (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to-everything (V2X). In other examples, the UE may not necessarily have a user in the sense of a human user who owns and / or operates the associated device. Alternatively, the UE may represent a device that is intended to be sold to or operated by a human user but may not or may not initially be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, the UE may represent a device that is not intended to be sold to or operated by an end user but may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0203] UE 1100 includes a processing circuit 1102, which is operatively coupled to an input / output interface 1106, a power supply 1108, a memory 1110, a communication interface 1112, and other components that may not be explicitly shown via a bus 1104. Fig.11 All or a subset of the components shown. The level of integration between components may vary from UE to UE. In addition, some UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0204] The processing circuit 1102 is configured to process instructions and data, and may be configured to implement any sequential state machine operable to execute instructions stored in the memory 1110 as a machine-readable computer program. The processing circuit 1102 may be implemented as: one or more hardware-implemented state machines (e.g., implemented in discrete logic, field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors (e.g., microprocessors or digital signal processors (DSPs)) together with appropriate software; or any combination of the above. For example, the processing circuit 1102 may include multiple central processing units (CPUs).

[0205] In this example, the input / output interface 1106 can be configured to provide one or more interfaces to an input device, an output device, or one or more input and / or output devices. Examples of output devices include speakers, sound cards, video cards, displays, monitors, printers, actuators, transmitters, smart cards, another output device, or any combination thereof. An input device can allow a user to capture information into the UE 1100. Examples of input devices include touch-sensitive or presence-sensitive displays, cameras (e.g., digital cameras, digital video cameras, web cameras, etc.), microphones, sensors, mice, trackballs, directional keyboards, trackpads, scroll wheels, smart cards, etc. The presence-sensitive display can include a capacitive or resistive touch sensor to sense input from a user. The sensor can be, for example, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device can use an interface port of the same type as an input device. For example, a universal serial bus (USB) port can be used to provide input devices and output devices.

[0206] In some embodiments, the power supply 1108 is configured as a battery or a battery pack. Other types of power supplies may be used, such as an external power supply (e.g., a power outlet), a photovoltaic device, or a battery. The power supply 1108 may also include a power supply circuit for delivering power from the power supply 1108 itself and / or an external power supply to various parts of the UE 1100 via an input circuit or an interface such as a power cable. The delivered power may be used, for example, to charge the power supply 1108. The power supply circuit may perform any formatting, conversion, or other modification on the power from the power supply 1108 so that the power is suitable for the various components of the UE 1100 to which it is powered.

[0207] The memory 1110 may be or be configured to include a memory, such as a random access memory (RAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic disk, an optical disk, a hard disk, a removable tape, a flash drive, etc. In one example, the memory 1110 includes one or more application programs 1114, such as an operating system, a web browser application, a widget, a gadget engine, or other applications, and corresponding data 1116. The memory 1110 may store any one or a combination of various operating systems used by the UE 1100.

[0208] The memory 1110 may be configured to include a plurality of physical drive units, such as a redundant array of independent disks (RAID), a flash memory, a USB flash drive, an external hard drive, a thumb drive, a pen drive, a key drive, a high-density digital versatile disc (HD-DVD) optical drive, a built-in hard drive, a Blu-ray optical drive, a holographic digital data storage (HDDS) optical drive, an external mini dual in-line memory module (DIMM), a synchronous dynamic random access memory (SDRAM), an external micro DIMM SDRAM, a smart card memory (e.g., a tamper-proof module in the form of a universal integrated circuit card (UICC), including one or more subscriber identity modules (SIMs), such as USIM and / or ISIM), other memories, or any combination thereof. The UICC may be, for example, an embedded UICC (eUICC), an integrated UICC (iUICC), or a removable UICC commonly referred to as a “SIM card”. The memory 1110 may allow the UE 1100 to access instructions, applications, etc. stored on a temporary or non-temporary storage medium to offload data or upload data. An article of manufacture, such as an article of manufacture utilizing a communication system, may be tangibly embodied as or in memory 1110 , which may be or include a device-readable storage medium.

[0209] The processing circuit 1102 may be configured to communicate with an access network or other network using a communication interface 1112. The communication interface 1112 may include one or more communication subsystems and may include an antenna 1122 or be communicatively coupled to the antenna 1122. The communication interface 1112 may include one or more transceivers for communication (e.g., by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network)). Each transceiver may include a transmitter 1118 and / or a receiver 1120 suitable for providing network communications (e.g., optical, electrical, frequency allocation, etc.). In addition, the transmitter 1118 and / or the receiver 1120 may be coupled to one or more antennas (e.g., 1122) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0210] In the illustrated embodiment, the communication functionality of the communication interface 1112 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communication such as Bluetooth, near field communication, location-based communication (e.g., using a global positioning system (GPS) to determine location), another type of communication functionality, or any combination thereof. Communication may be implemented according to one or more communication protocols and / or standards (e.g., IEEE 802.11, code division multiple access (CDMA), wideband code division multiple access (WCDMA), GSM, LTE, new radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / Internet protocol (TCP / IP), synchronous optical network (SONET), asynchronous transfer mode (ATM), QUIC, hypertext transfer protocol (HTTP), etc.).

[0211] Regardless of the type of sensor, the UE may provide an output of data captured by its sensor through its communication interface 1112 via a wireless connection to a network node. The data captured by the UE's sensor may be transmitted via another UE via a wireless connection to a network node. The output may be periodic (e.g., every 15 minutes if it reports the sensed temperature), random (e.g., to balance the load of reports from several sensors), in response to a trigger event (e.g., sending an alarm when humidity is detected), in response to a request (e.g., a user-initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0212] As another example, the UE includes an actuator, motor, or switch associated with a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input, the state of the actuator, motor, or switch can change. For example, the UE can include a motor that adjusts a control surface or rotor of a drone in flight based on the received input, or adjusts a robotic arm performing a medical procedure based on the received input.

[0213] When the UE is in the form of an Internet of Things (IoT) device, the UE can be a device used in one or more application areas, including but not limited to urban wearable technology, extended industrial applications, and healthcare. Non-limiting examples of such IoT devices are the following devices or devices embedded in the following devices: connected refrigerators or freezers, televisions, connected lighting devices, electricity meters, robotic vacuum cleaners, voice-controlled smart speakers, home security cameras, motion detectors, thermostats, smoke detectors, door and window sensors, flood / humidity sensors, electronic door locks, connected doorbells, air conditioning systems (such as heat pumps), autonomous vehicles, monitoring systems, weather monitoring devices, vehicle parking monitoring devices, electric vehicle charging stations, smart watches, fitness trackers, head-mounted displays for augmented reality (AR) or virtual reality (VR), wearable devices for tactile enhancement or sensory enhancement, sprinklers, animal tracking or item tracking devices, sensors for monitoring plants or animals, industrial robots, unmanned aerial vehicles (UAVs), and any kind of medical equipment (such as heart rate monitors or teleoperated surgical robots). In addition to the above, Fig.11 In addition to the other components depicted in the illustrated UE 1100 , a UE in the form of an IoT device may include circuitry and / or software depending on the intended application of the IoT device.

[0214] As another specific example, in an IoT scenario, a UE may represent a machine or other device that performs monitoring and / or measurement and sends the results of such monitoring and / or measurement to another UE and / or a network node. In this case, the UE may be an M2M device, which may be referred to as an MTC device in the 3GPP context. The UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle (e.g., a car, bus, truck, ship, and airplane) or other device that is capable of monitoring and / or reporting its operating status or other functions associated with its operation.

[0215] In fact, for a single use case, any number of UEs may be used together. For example, a first UE may be a drone or integrated in a drone and provide the drone's speed information (obtained via a speed sensor) to a second UE, which is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g., by controlling an actuator) to increase or decrease the drone's speed. The first UE and / or the second UE may also include more than one of the above functions. For example, a UE may include a sensor and an actuator and handle data communications for both the speed sensor and the actuator.

[0216] Fig.12 A network node 1200 according to some embodiments is shown. Examples of network nodes include, but are not limited to, access points (eg, radio access points) and base stations (eg, radio base stations, NodeBs, eNBs, and gNBs).

[0217] Base stations may be classified based on the amount of coverage they provide (or in other words, their transmit power level), and thus may be referred to as femto, pico, micro or macro base stations, depending on the amount of coverage provided. A base station may be a relay node or a relay donor node that controls a relay. A network node may also include one or more (or all) parts of a distributed radio base station, such as a centralized digital unit and / or a remote radio unit (RRU), sometimes referred to as a remote radio head (RRH). These remote radio units may or may not be integrated with an antenna as an antenna-integrated radio. Portions of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0218] Other examples of network nodes include a multi-transmission point (multi-TRP) 5G access node, a multi-standard radio (MSR) device (e.g., an MSR BS), a network controller (e.g., a radio network controller (RNC) or a base station controller (BSC)), a base transceiver station (BTS), a transmission point, a transmission node, a multi-cell / multicast coordination entity (MCE), an operation and maintenance (O&M) node, an operation support system (OSS) node, a self-organizing network (SON) node, a positioning node (e.g., an evolved serving mobile location center (E-SMLC)), and / or a minimization of drive tests (MDT).

[0219] For example, one or more network nodes 1200 may be configured to perform operations attributed to the NWDAF (or its logical functions) in the description of the present invention of various methods or processes. As a more specific example, one or more network nodes 1200 may be configured to perform operations attributed to a consumer NF (e.g., NWDAF AnLF), a producer NF (e.g., NWDAF MTLF), an NRF, and an ADRF.

[0220] The network node 1200 includes a processing circuit 1202, a memory 1204, a communication interface 1206, and a power supply 1208. The network node 1200 may be composed of multiple physically separated components (e.g., NodeB components and RNC components, BTS components and BSC components, etc.), which may have their own corresponding components. In certain scenarios where the network node 1200 includes multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared between multiple network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique "NodeB and RNC pair" may be considered a single separate network node in some cases. In some embodiments, the network node 1200 may be configured to support multiple radio access technologies (RATs). In such an embodiment, some components may be replicated (e.g., separate memories 1204 for different RATs), and some components may be reused (e.g., the same antenna 1210 may be shared by different RATs). The network node 1200 may also include multiple sets of the various components shown for different wireless technologies (e.g., GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, radio frequency identification (RFID), or Bluetooth wireless technologies) integrated into the network node 1200. These wireless technologies may be integrated into the same or different chips or chipsets and other components within the network node 1200.

[0221] The processing circuit 1202 may include a combination of one or more of the following: a microprocessor, a controller, a microcontroller, a central processing unit, a digital signal processor, an application specific integrated circuit, a field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic that is operable to provide network node 1200 functionality alone or in combination with other network node 1200 components (e.g., memory 1204).

[0222] In some embodiments, processing circuitry 1202 includes a system on a chip (SOC). In some embodiments, processing circuitry 1202 includes radio frequency (RF) transceiver circuitry 1212 and / or baseband processing circuitry 1214. In some embodiments, RF transceiver circuitry 1212 and baseband processing circuitry 1214 may be on separate chips (or chipsets), boards, or units (e.g., a radio unit and a digital unit). In alternative embodiments, part or all of RF transceiver circuitry 1212 and / or baseband processing circuitry 1214 may be on the same chip or chipset, board, or unit set.

[0223] The memory 1204 may include any form of volatile or non-volatile computer-readable memory, including but not limited to permanent storage devices, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (e.g., hard disk), removable storage media (e.g., flash drive, compact disk (CD) or digital video disk (DVD)), and / or any other volatile memory or non-volatile, non-transitory device-readable and / or computer-executable memory device that stores information, data and / or instructions that can be used by the processing circuit 1202. The memory 1204 may store any suitable instructions, data or information, including computer programs, software, applications including one or more of logic, rules, codes, tables, and / or other instructions that can be executed by the processing circuit 1202 and used by the network node 1200. The storage device 1204 may be used to store any calculations made by the processing circuit 1202 and / or any data received via the communication interface 1206. In some embodiments, the processing circuit 1202 and the memory 1204 are integrated together.

[0224] The communication interface 1206 is used for wired or wireless communication of signaling and / or data between network nodes, access networks and / or UEs. As shown, the communication interface 1206 includes a port / terminal 1216 for sending data to and receiving data from the network, for example, via a wired connection. The communication interface 1206 also includes a radio front-end circuit 1218, which can be coupled to the antenna 1210, or in some embodiments is a part of the antenna 1210. The radio front-end circuit 1218 includes a filter 1220 and an amplifier 1222. The radio front-end circuit 1218 can be connected to the antenna 1210 and the processing circuit 1202. The radio front-end circuit can be configured to adjust the signal transmitted between the antenna 1210 and the processing circuit 1202. The radio front-end circuit 1218 can receive digital data to be sent to other network nodes or UEs via a wireless connection. The radio front-end circuit 1218 can use a combination of a filter 1220 and / or an amplifier 1222 to convert the digital data into a radio signal having suitable channel and bandwidth parameters. The radio signal can then be sent via the antenna 1210. Similarly, when receiving data, antenna 1210 may collect radio signals, which may then be converted to digital data by radio front end circuitry 1218. The digital data may be passed to processing circuitry 1202. In other embodiments, the communication interface may include different components and / or different combinations of components.

[0225] In some alternative embodiments, the network node 1200 does not include a separate radio front end circuit 1218, instead the processing circuit 1202 includes the radio front end circuit and is connected to the antenna 1210. Similarly, in some embodiments, all or some of the RF transceiver circuit 1212 is part of the communication interface 1206. In yet another embodiment, the communication interface 1206 includes one or more ports or terminals 1216, the radio front end circuit 1218, and the RF transceiver circuit 1212 as part of a radio unit (not shown), and the communication interface 1206 communicates with the baseband processing circuit 1214, which is part of the digital unit (not shown).

[0226] Antenna 1210 may include one or more antennas or antenna arrays configured to send and / or receive wireless signals. Antenna 1210 may be coupled to radio front end circuit 1218 and may be any type of antenna capable of wirelessly sending and receiving data and / or signals. In some embodiments, antenna 1210 is separate from network node 1200 and may be connected to network node 1200 via an interface or port.

[0227] Antenna 1210, communication interface 1206 and / or processing circuit 1202 may be configured to perform any receiving operation and / or certain obtaining operations performed by a network node as described herein. Any information, data and / or signal may be received from a UE, another network node and / or any other network device. Similarly, antenna 1210, communication interface 1206 and / or processing circuit 1202 may be configured to perform any sending operation performed by a network node as described herein. Any information, data and / or signal may be sent to a UE, another network node and / or any other network device.

[0228] The power supply 1208 provides power to the various components of the network node 1200 in a form suitable for the various components (e.g., at the voltage and current levels required by each corresponding component). The power supply 1208 may also include power management circuitry or be coupled to power management circuitry to supply power to the components of the network node 1200 for performing the functions described herein. For example, the network node 1200 may be connected to an external power source (e.g., a power grid, a power outlet) via an input circuit or interface (e.g., a cable), whereby the external power source supplies power to the power circuit of the power supply 1208. As another example, the power supply 1208 may include a power source in the form of a battery or a battery pack, which is connected to the power circuit or integrated in the power circuit. If the external power source fails, the battery can provide backup power.

[0229] Embodiments of network node 1200 may include beyond Fig.12 Additional components to the components shown are used to provide certain aspects of the functionality of the network node (including any functionality described herein and / or any functionality required to support the subject matter described herein). For example, the network node 1200 may include a user interface device to allow information to be input into the network node 1200 and to allow information to be output from the network node 1200. This may allow a user to perform diagnostic, maintenance, repair, and other management functions for the network node 1200.

[0230] Fig.13 is a block diagram of a host 1300 according to various aspects described herein, which host 1300 may be Fig.10 As used herein, host 1300 may be or include various combinations of hardware and / or software (including processing resources in a standalone server, blade server, cloud-implemented server, distributed server, virtual machine, container, or server cluster). Host 1300 may provide one or more services to one or more UEs.

[0231] Host 1300 includes processing circuitry 1302, which is operably coupled to input / output interface 1306, network interface 1308, power supply 1310, and memory 1312 via bus 1304. Other components may be included in other embodiments. The features of these components may be substantially similar to those described with respect to the previous figures (e.g., Fig.11 and Fig.12 ) so that its description is generally applicable to corresponding components of the host 1300.

[0232] The memory 1312 may include one or more computer programs including data 1316 and one or more host applications 1314, which may include user data, such as data generated by a UE for the host 1300, or data generated by the host 1300 for the UE. An embodiment of the host 1300 may utilize only a subset or all of the components shown. The host application 1314 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FLAC, Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for a variety of different categories, types, or implementations of UE (e.g., mobile phones, desktop computers, wearable display systems, head-up display systems). The host application 1314 may also provide user authentication and permission checks, and may periodically report health status, routing, and content availability to a central node (such as a device in a core network or a device on the edge of a core network). Thus, the host 1300 can select and / or indicate to the UE a different host for an over-the-top service. The host application 1314 can support various protocols, such as HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (MPEG-DASH), etc.

[0233] Fig.141400 is a block diagram showing a virtualized environment 1400 in which the functions implemented by some embodiments may be virtualized. In this context, virtualization means creating a virtual version of an apparatus or device that may include a virtualized hardware platform, storage device, and network resources. As used herein, virtualization may be applied to any device or component thereof described herein, and relates to an implementation in which at least a portion of the functions are implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 1400 hosted by one or more of a hardware node (e.g., a hardware computing device operating as a network node, UE, core network node, or host). In addition, in an embodiment where a virtual node does not require a radio connection (e.g., a core network node or host), the node may be fully virtualized.

[0234] Application 1402 (which may alternatively be referred to as a software instance, a virtual application, a network function, a virtual node, a virtual network function, etc.) operates in virtualized environment 1400 to implement some features, functions, and / or benefits of some embodiments disclosed herein.

[0235] For example, various NFs (or portions thereof) described herein with respect to other figures may be implemented as virtual network functions 1402 in the virtualized environment 1400. As a more specific example, a consumer NF (e.g., NWDAF AnLF), a producer NF (e.g., NWDAF MTLF), an NRF, and / or an ADRF may be implemented as a virtual network function 1402 in the virtualized environment 1400.

[0236] Hardware 1404 includes processing circuitry, memory storing software and / or instructions that can be executed by the hardware processing circuitry, and / or other hardware devices described herein (such as network interfaces, input / output interfaces, etc.). Software can be executed by the processing circuitry to instantiate one or more virtualization layers 1406 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 1408a and VMs 1408b (one or more of which can be generally referred to as VMs 1408), and / or perform any functions, features, and / or benefits described in connection with some embodiments described herein. Virtualization layer 1406 can present a virtual operating platform to VMs 1408 that looks like network hardware.

[0237] VM 1408 includes virtual processing, virtual memory, virtual network or interface, and virtual storage, and can be run by corresponding virtualization layer 1406. Different embodiments of instances of virtual device 1402 can be implemented on one or more VMs 1408, and these implementations can be made in different ways. In some contexts, virtualization of hardware is referred to as network function virtualization (NFV). NFV can be used to unify numerous network device types onto industry standard high-capacity server hardware, physical switches, and physical storage that can be located in data centers and customer premises equipment (CPE).

[0238] In the context of NFV, VM 1408 can be a software implementation of a physical machine that runs programs as if they were executed on a physical, non-virtualized machine. Each VM 1408 and the portion of hardware 1404 that executes the VM (whether it is hardware dedicated to the VM and / or hardware shared by the VM with other VMs) form a separate virtual network element. Still in the context of NFV, a virtual network function is responsible for handling a specific network function running in one or more VMs 1408 on hardware 1404 and corresponding to an application 1402.

[0239] Hardware 1404 may be implemented in a standalone network node with general or specific components. Hardware 1404 may implement some functions via virtualization. Alternatively, hardware 1404 may be part of a larger hardware cluster (e.g., in a data center or CPE), where many hardware nodes work together and are managed by management and orchestration 1410, which in particular oversees the lifecycle management of application 1402. In some embodiments, hardware 1404 is coupled to one or more radio units, each of which includes one or more transmitters and one or more receivers that may be coupled to one or more antennas. The radio unit may communicate directly with other hardware nodes via one or more appropriate network interfaces, and may be used in conjunction with virtual components to provide radio capabilities to virtual nodes (e.g., radio access nodes or base stations). In some embodiments, some signaling may be provided by using a control system 1412, which may alternatively be used for communication between hardware nodes and radio units.

[0240] Fig.15 A communication diagram is shown in which a host 1502 communicates with a UE 1506 via a network node 1504 over a partially wireless connection according to some embodiments. Fig.15 Describe the UE discussed in the previous paragraphs (e.g., Fig.10 UE 1012a and / or Fig.11 UE 1100), network node (e.g., Fig.10The network node 1010a and / or Fig.12 network node 1200) and a host (e.g., Fig.10 Host 1016 and / or Fig.13 An example implementation of a host 1300 according to various embodiments.

[0241] Similar to the host 1300, an embodiment of the host 1502 includes hardware, such as a communication interface, a processing circuit, and a memory. The host 1502 also includes software that is stored in the host 1502 or can be accessed by the host 1502 and can be executed by the processing circuit. The software includes a host application that is operable to provide services to a remote user, such as a UE 1506 connected via an over-the-top (OTT) connection 1550 extending between the UE 1506 and the host 1502. When providing services to the remote user, the host application can provide user data sent using the OTT connection 1550.

[0242] The network node 1504 includes hardware that enables it to communicate with the host 1502 and the UE 1506. The connection 1560 can be a direct connection or through a core network (such as Fig.10 The core network 1006 of the present invention) and / or one or more other intermediate networks (eg, one or more public, private, or managed networks). For example, the intermediate network may be a backbone network or the Internet.

[0243] UE 1506 includes hardware and software that is stored in or accessible by UE 1506 and can be executed by the processing circuitry of the UE. The software includes a client application (e.g., a web browser or an operator-specific "app") that is operable to provide services to a human or non-human user via UE 1506 with the support of host 1502. In host 1502, an executing host application can communicate with an executing client application via an OTT connection 1550, which terminates at UE 1506 and host 1502. When providing services to a user, the client application of the UE can receive request data from the host application of the host and provide user data in response to the request data. The OTT connection 1550 can transmit both request data and user data. The client application of the UE can interact with the user to generate user data provided to the host application via the OTT connection 1550.

[0244] The OTT connection 1550 may extend via a connection 1560 between the host 1502 and the network node 1504 and via a wireless connection 1570 between the network node 1504 and the UE 1506 to provide connectivity between the host 1502 and the UE 1506. The connection 1560 and the wireless connection 1570 through which the OTT connection 1550 may be provided have been drawn abstractly to illustrate communications between the host 1502 and the UE 1506 via the network node 1504 without explicitly involving any intermediate devices and the precise routing of messages via those devices.

[0245] As an example of sending data via OTT connection 1550, in step 1508, host 1502 provides user data, which can be performed by executing a host application. In some embodiments, the user data is associated with a specific human user who interacts with UE 1506. In other embodiments, the user data is associated with UE 1506, which shares data with host 1502 without explicit human interaction. In step 1510, host 1502 initiates a transmission to UE 1506, which carries the user data. Host 1502 may initiate the transmission in response to a request sent by UE 1506. The request may be caused by human interaction with UE 1506 or by the operation of a client application executed on UE 1506. According to the teachings of the embodiments described throughout the present disclosure, the transmission may be transmitted via network node 1504. Thus, in step 1512, in accordance with the teachings of the embodiments described throughout the present disclosure, network node 1504 sends the user data carried in the transmission initiated by host 1502 to UE 1506. In step 1514, UE 1506 receives the user data carried in the transmission, which may be performed by a client application executing on UE 1506 that is associated with a host application executed by host 1502.

[0246] In some examples, UE 1506 executes a client application that provides user data to host 1502. User data may be provided as a reaction or response to data received from host 1502. Therefore, in step 1516, UE 1506 may provide user data, which may be performed by executing the client application. When providing user data, the client application may also take into account user input received from a user via an input / output interface of UE 1506. Regardless of the specific manner in which user data is provided, UE 1506 initiates transmission of user data to host 1502 via network node 1504 in step 1518. In step 1520, network node 1504 receives user data from UE 1506 and initiates transmission of the received user data to host 1502 in accordance with the teachings of the embodiments described throughout the present disclosure. In step 1522, host 1502 receives user data carried in the transmission initiated by UE 1506.

[0247] One or more of the various embodiments improves the performance of OTT services provided to UE 1506 using OTT connection 1550, in which wireless connection 1570 forms the final part. For example, by providing the owner / producer of the AI / ML model with the ability to protect the AI / ML model during various transmission, storage and retrieval scenarios, the embodiment improves the security of confidential and / or sensitive AI / ML models, thereby facilitating the deployment of such models in multi-vendor communication networks (e.g., 5GC). In this way, the embodiment facilitates the use of deployed AI / ML models to improve network performance, thereby increasing the value of OTT services delivered through the network improved in this manner.

[0248] In an example scenario, host 1502 may collect and analyze plant status information. As another example, host 1502 may process audio and video data that may have been retrieved from a UE for use in creating a map. As another example, host 1502 may collect and analyze real-time data to help control vehicle congestion (e.g., control traffic lights). As another example, host 1502 may store monitoring videos uploaded by a UE. As another example, host 1502 may store or control access to media content such as video, audio, VR, or AR, which may be broadcast, multicast, or unicast to a UE. As other examples, host 1502 may be used for energy pricing, remote control of non-time-critical electrical loads to balance power generation demand, positioning services, presentation services (e.g., compiling charts based on data collected from remote devices, etc.), or any other function of collecting, retrieving, storing, analyzing, and / or sending data.

[0249] In some examples, a measurement process may be provided for monitoring data rate, latency, and other factors that are the object of improvement of one or more embodiments. There may also be an optional network function for reconfiguring the OTT connection 1550 between the host 1502 and the UE 1506 in response to changes in the measurement results. The measurement process and / or the network function for reconfiguring the OTT connection may be implemented in the software and hardware of the host 1502 and / or the UE 1506. In some embodiments, sensors (not shown) may be deployed in other devices through which the OTT connection 1550 passes, or associated with the other devices; the sensors may participate in the measurement process by providing the values ​​of the monitoring quantities exemplified above or providing the values ​​of other physical quantities from which the software can calculate or estimate the monitoring quantities. Reconfiguring the OTT connection 1550 may include message formats, retransmission settings, preferred routes, etc.; reconfiguration does not require direct changes to the operation of the network node 1504. Such processes and functions may be known and practiced in the art. In some embodiments, the measurement may involve proprietary UE signaling that facilitates the host 1502 to measure throughput, propagation time, latency, etc. Measurements can be made by software using the OTT connection 1550 to send messages (particularly empty or "dummy" messages) while monitoring propagation times, errors, etc.

[0250] The above merely illustrates the principles of the present disclosure. In view of the teachings herein, various modifications and changes to the described embodiments will be apparent to those skilled in the art. Therefore, it should be understood that those skilled in the art will be able to design many systems, arrangements and programs that, although not explicitly shown or described herein, embody the principles of the present disclosure and therefore can be used within the spirit and scope of the present disclosure. As will be understood by those of ordinary skill in the art, the various embodiments can be used together or interchangeably.

[0251] The term unit as used herein may have a conventional meaning in the field of electronics, electrical devices and / or electronic devices, and may include, for example, electrical and / or electronic circuits, devices, modules, processors, memories, logical solid-state and / or discrete devices, computer programs or instructions for performing various tasks, processes, calculations, output and / or display functions, etc., such as those described herein.

[0252] Any suitable steps, methods, features, functions or benefits disclosed herein may be performed by one or more functional units or modules of one or more virtual devices. Each virtual device may include multiple of these functional units. These functional units may be implemented by processing circuits, which may include one or more microprocessors or microcontrollers and other digital hardware (which may include digital signal processors (DSPs), dedicated digital logic, etc.). The processing circuit may be configured to execute program codes stored in a memory, which may include one or more types of memory, such as read-only memory (ROM), random access memory (RAM), cache memory, flash memory device, optical storage device, etc. The program code stored in the memory includes program instructions for executing one or more telecommunications and / or data communication protocols and instructions for executing one or more technologies described herein. In some implementations, the processing circuit may be used to cause each functional unit to perform a corresponding function according to one or embodiments of the present disclosure.

[0253] As described herein, a device and / or apparatus may be represented by a semiconductor chip, a chipset, or a (hardware) module including such a chip or chipset; however, this does not exclude the possibility that the functionality of the device or apparatus is not implemented by hardware but is implemented as a software module (e.g., a computer program or computer program product including an executable software code portion for execution or operation on a processor). In addition, the functionality of the device or apparatus may be implemented by any combination of hardware and software. A device or apparatus may also be considered as a combination of multiple devices and / or apparatuses, whether they functionally cooperate with each other or are independent of each other. In addition, as long as the functionality of the device or apparatus is retained, the device and apparatus may be implemented in a distributed manner throughout the system. This principle and similar principles are considered to be known to technicians.

[0254] Unless otherwise defined, all terms used herein, including technical and scientific terms, have the same meanings as those commonly understood by those of ordinary skill in the art to which the present disclosure belongs. It will be further understood that the terms used herein should be interpreted as being consistent with their meanings in the context of this specification and the relevant technology, and not interpreted as ideal or overly formal meanings, unless so explicitly defined herein.

[0255] In addition, certain terms used in this disclosure (including the specification and the drawings) may be used synonymously in certain instances (e.g., "data" and "information"). It should be understood that although these terms and / or their terms that may be synonymous with each other may be used synonymously herein, there may be instances where such words may not be intended to be used synonymously.

[0256] Example embodiments of the techniques and apparatus described herein include, but are not limited to, the following:

[0257] A1. A method for a consumer network function (NFc) of a communication network, the method comprising:

[0258] sending a first request for a first access token associated with a machine learning (ML) model to a first NF of a communication network, wherein the first request includes one or more of the following items associated with the ML model: an analysis identifier (ID) and an interoperability ID;

[0259] receiving a first response including a first access token from the first NF;

[0260] sending a second request for the ML model to a producer NF (NFp) of the communication network, wherein the second request includes at least one of the analysis ID and the interoperability ID and the first access token; and

[0261] A second response is received from the NFp including one of the following:

[0262] ML models, or

[0263] A universal resource locator (URL) of a storage resource associated with a second NF of the communication network, from which the ML model can be obtained.

[0264] A2. The method of embodiment A1, wherein the first NF is one of: a network repository function (NRF) or an analytical data repository function (ADRF).

[0265] A3. The method of any one of embodiments A1 to A2, wherein one or more of the following applies:

[0266] NFc is the analysis logic function of the network data analysis function NWDAF (AnLF); and

[0267] NFp is the model training logic function NWDAF (MTLF) of the network data analysis function.

[0268] A4. The method according to any one of embodiments A1 to A3, wherein:

[0269] The second response includes the ML model, the ML model being encrypted; and

[0270] The second response also includes information that can be used to locate a key that can be used to decrypt and verify the ML model.

[0271] A5. The method according to any one of embodiments A1 to A3, wherein:

[0272] The second response includes a URL; and

[0273] The method further includes:

[0274] sending a third request for a second access token associated with the ML model to the first NF, wherein the third request includes one or more of the analysis ID and the interoperability ID and the URL;

[0275] receiving a third response including the second access token from the first NF; and

[0276] Get the ML model from the second NF using the second access token and URL.

[0277] A6. The method of embodiment A5, wherein the second NF from which the ML model is obtained using the URL is one of: a producer NF of a communication network or an analytical data repository function (ADRF).

[0278] A7. A method according to any one of embodiments A5 to A6, wherein the URL is encrypted and the second response also includes information that can be used to locate a key that can be used to decrypt and verify the URL.

[0279] B1. A method for a producer network function (NFp) of a communication network, the method comprising:

[0280] Registering information associated with a machine learning (ML) model in a network repository function (NRF) of a communication network, wherein:

[0281] The ML model is produced, owned and / or maintained by the NFp, and

[0282] The registered information includes the following items: an analysis identifier (ID); an interoperability ID; and a first universal resource locator (URL) of a storage resource associated with the NFp, from which the ML model can be obtained; and

[0283] The ML model is encrypted and a first request for storing the encrypted ML model is sent to an analytics data repository function (ADRF) of the communication network, wherein the first request includes the encrypted ML model or a first URL.

[0284] B2. The method according to embodiment B1, further comprising:

[0285] receiving a second request for the ML model from a consumer NF (NFc) of the communication network, wherein the second request includes at least one of the analysis ID and the interoperability ID and the first access token; and

[0286] Based on verifying the first access token, a second response is sent to the NFc, the second response including one of the following items: the ML model; the first URL; or a second URL of a storage resource associated with the ADRF, from which the ML model can be obtained.

[0287] B3. The method according to embodiment B2, wherein:

[0288] The first request includes a first URL; and

[0289] The second response includes the first URL or the second URL.

[0290] B4. The method of embodiment B3, wherein one or more of the following applies:

[0291] The first URL in the first request is encrypted, and the first request also includes information that can be used to locate a key that can be used to decrypt and verify the first URL; and

[0292] The first URL or the second URL included in the second response is encrypted, and the second response also includes information that can be used to locate a key that can be used to decrypt and verify the first URL or the second URL.

[0293] B5. The method according to any one of embodiments B3 to B4, further comprising:

[0294] receiving another request for the ML model from the ADRF, wherein the another request includes the first URL and the second access token;

[0295] Based on validating the second access token, sending another response including the encrypted ML model to the ADRF; and

[0296] The second URL is then received from the ADRF and the registration information in the NRF is updated to include the second URL.

[0297] B6. The method according to any one of embodiments B3 to B5, wherein:

[0298] The second response includes the first URL; and

[0299] The method further includes:

[0300] receiving a third request for the ML model from the NFc, wherein the third request includes: a third access token associated with the ML model, the first URL, and one or more of an analysis ID and an interoperability ID; and

[0301] Based on verifying the third access token, a third response including the ML model is sent to the NFc.

[0302] B7. The method of embodiment B6, wherein the ML model in the third response is encrypted, and the third response further includes information that can be used to locate a key that can be used to decrypt and verify the ML model.

[0303] B8. The method according to embodiment B2 further comprising:

[0304] sending a fourth request for an access token associated with the ML model to a network repository function (NRF) of the communication network, wherein the fourth request includes one or more of the analysis ID and the interoperability ID;

[0305] Receive the requested access token from NRF;

[0306] sending a fifth request for the ML model to the ADRF, wherein the fifth request includes one or more of the analysis ID and the interoperability ID and the received access token;

[0307] A fifth response including the ML model is received from the ADRF, and the ML model is then included in the second response to the NFc.

[0308] B9. The method of any one of embodiments B1 to B8, wherein one or more of the following applies:

[0309] NFc is the analysis logic function of the network data analysis function NWDAF (AnLF); and

[0310] NFp is the model training logic function NWDAF (MTLF) of the network data analysis function.

[0311] C1. A method for a network repository function (NRF) of a communication network, the method comprising:

[0312] registering information associated with a machine learning (ML) model produced, owned and / or maintained by a producer network function (NFp) of a communication network, wherein the registered information includes the following items associated with the ML model: an analysis identifier (ID); an interoperability ID; and a first universal resource locator (URL) of a storage resource associated with the NFp from which the ML model can be obtained; and

[0313] receiving a first request for a first access token associated with the ML model from a consumer NF (NFc) of the communication network, wherein the first request includes one or more of an analysis ID and an interoperability ID;

[0314] A first response including a first access token is sent to the NFc.

[0315] C2. The method according to embodiment C1, further comprising:

[0316] A second request for a second access token is received from the first NF of the communication network, wherein the second request includes the following items:

[0317] One or more of an analysis ID and an interoperability ID; and

[0318] a first URL or a second URL of a storage resource associated with an analytics data repository function (ADRF) of the communication network from which the ML model is available; and

[0319] A second response including a second access token is sent to the first NF.

[0320] C3. The method of embodiment C2, wherein the first NF is one of the following: NFc or NFp.

[0321] C4. The method of any one of embodiments C2 to C3, further comprising: after registering the information associated with the ML model, updating the registered information to include the second URL.

[0322] C5. The method of any one of embodiments C1 to C4, wherein one or more of the following applies:

[0323] NFc is the analysis logic function of the network data analysis function NWDAF (AnLF); and

[0324] NFp is the model training logic function NWDAF (MTLF) of the network data analysis function.

[0325] D1. A method for analyzing a data repository function (ADRF) of a communication network, the method comprising:

[0326] receiving a first request for storing an encrypted machine learning (ML) model from a producer network function (NFp) of a communication network, wherein the first request includes a first universal resource locator (URL) of the encrypted ML model or a storage resource associated with the NFp from which the encrypted ML model can be obtained;

[0327] storing the encrypted ML model in a storage resource associated with the ADRF; and

[0328] A first response is sent to the NFp, the first response including a second URL of a storage resource associated with the ADRF.

[0329] D2. The method according to embodiment D1, wherein:

[0330] The first request includes a first URL; and

[0331] The method further includes:

[0332] sending another request for the ML model to the NFp, wherein the another request includes the first URL and the second access token; and

[0333] Another response is received from the NFp including the encrypted ML model.

[0334] D3. The method according to embodiment D1, further comprising:

[0335] receiving a second request for the ML model from the communicating first NF, wherein the second request includes one or more of the analysis ID and the interoperability ID and a third access token;

[0336] Based on verifying the third access token, a second response including the ML model is sent to the first NF.

[0337] D4. The method of embodiment D3, wherein the first NF is one of: a NFc of a communication network or a consumer NF (NFc).

[0338] D5. The method of embodiment D4, wherein one or more of the following applies:

[0339] NFc is the analysis logic function of the network data analysis function NWDAF (AnLF); and

[0340] NFp is the model training logic function NWDAF (MTLF) of the network data analysis function.

[0341] D6. The method of any one of embodiments D3 to D5, wherein the ML model in the second response is encrypted, and the second response further includes information that can be used to locate a key that can be used to decrypt and verify the ML model.

[0342] E1. A consumer network function (NFc) of a communications network, wherein:

[0343] NFc is implemented by a communication interface circuit and a processing circuit that are operably coupled, and

[0344] The processing circuit and the interface circuit are configured to perform operations corresponding to any of the methods described according to embodiments A1 to A7.

[0345] E2. A consumer network function (NFc) of a communication network, the NFc being configured to perform operations corresponding to any of the methods according to embodiments A1 to A7.

[0346] E3. A non-transitory computer-readable medium storing computer-executable instructions which, when executed by a processing circuit associated with a consumer network function (NFc) of a communication network, configure the NFc to perform operations corresponding to any of the methods described in embodiments A1 to A7.

[0347] E4. A computer program product comprising computer executable instructions which, when executed by a processing circuit associated with a consumer network function (NFc) of a communication network, configure the NFc to perform operations corresponding to any of the methods according to embodiments A1 to A7.

[0348] F1. A producer network function (NFp) of a communications network, wherein:

[0349] The NFp is implemented by operatively coupled communication interface circuitry and processing circuitry; and

[0350] The processing circuit and the interface circuit are configured to perform operations corresponding to any of the methods described according to embodiments B1 to B9.

[0351] F2. A producer network function (NFp) of a communication network, the NFp being configured to perform operations corresponding to any of the methods described according to embodiments B1 to B9.

[0352] F3. A non-transitory computer-readable medium storing computer-executable instructions which, when executed by a processing circuit associated with a producer network function (NFp) of a communication network, configure the NFp to perform operations corresponding to any of the methods described according to embodiments B1 to B9.

[0353] F4. A computer program product comprising computer executable instructions which, when executed by a processing circuit associated with a producer network function (NFp) of a communication network, configure the NFp to perform operations corresponding to any of the methods described according to embodiments B1 to B9.

[0354] G1. A network repository function (NRF) of a communications network, wherein:

[0355] The NRF is implemented by a communication interface circuit and a processing circuit that are operably coupled, and

[0356] The processing circuit and the interface circuit are configured to perform operations corresponding to any of the methods described according to embodiments C1 to C5.

[0357] G2. A network repository function (NRF) of a communication network, the NRF being configured to perform operations corresponding to any of the methods described according to embodiments C1 to C5.

[0358] G3. A non-transitory computer-readable medium storing computer-executable instructions which, when executed by a processing circuit associated with a network repository function (NRF) of a communication network, configure the NRF to perform operations corresponding to any of the methods described in embodiments C1 to C5.

[0359] G4. A computer program product comprising computer executable instructions which, when executed by a processing circuit associated with a network repository function (NRF) of a communication network, configure the NRF to perform operations corresponding to any of the methods described in embodiments C1 to C5.

[0360] H1. An analytical data repository function (ADRF) for a communication network, wherein:

[0361] ADRF is implemented by a communication interface circuit and a processing circuit that are operably coupled, and

[0362] The processing circuit and the interface circuit are configured to perform operations corresponding to any of the methods described according to embodiments D1 to D6.

[0363] H2. An analysis data repository function (ADRF) of a communication network, the ADRF being configured to perform operations corresponding to any of the methods described according to embodiments D1 to D6.

[0364] H3. A non-transitory computer-readable medium storing computer-executable instructions which, when executed by processing circuitry associated with an analysis data repository function (ADRF) of a communication network, configure the ADRF to perform operations corresponding to any of the methods described in embodiments D1 to D6.

[0365] H4. A computer program product comprising computer executable instructions which, when executed by a processing circuit associated with an analysis data repository function (ADRF) of a communication network, configure the ADRF to perform operations corresponding to any of the methods described in embodiments D1 to D6.

Claims

1. A method for a consumer network function NFc of a communication network, the method comprising: sending (610) a first request for a first access token associated with a machine learning ML model to a first network function NF of the communication network, wherein the first request comprises at least one of the following items associated with the ML model: an analysis identifier ID and an interoperability ID; receiving (620) a first response including the first access token from the first NF; sending (630) a second request for the ML model to a producer NF "NFp" of the communication network, wherein the second request comprises at least one of the analysis ID and the interoperability ID and the first access token; and A second response is received (640) from the NFp including one or more of: The ML model, the identifier of the ML model, and An address of a storage resource associated with a second NF of the communication network, from which the ML model is accessible.

2. The method according to claim 1, wherein: The first NF is one of the following: a network repository function NRF; or an analysis data repository function ADRF.

3. The method according to any one of claims 1 to 2, wherein: One or more of the following applies: The NFc is an analysis logic function of a network data analysis function NWDAF (AnLF); and The NFp is the model training logic function NWDAF (MTLF) of the network data analysis function.

4. The method according to any one of claims 1 to 3, wherein: The second response includes the ML model, the ML model being encrypted; and The second response also includes information that can be used to locate a key that can be used to decrypt and verify the ML model.

5. The method according to any one of claims 1 to 3, wherein: The second response includes an address of a storage resource associated with the second NF; as well as The method further comprises: sending (650) a third request for a second access token associated with the ML model to the first NF, wherein the third request includes the following: an address of a storage resource associated with the second NF, and at least one of the analysis ID and the interoperability ID; receiving (660) a third response including the second access token from the first NF; and The ML model is obtained (670) from the second NF using the second access token and an address of a storage resource associated with the second NF.

6. The method according to claim 5, wherein: The address of the storage resource is encrypted, and the second response also includes information that can be used to locate a key that can be used to decrypt and verify the address of the storage resource.

7. The method according to any one of claims 1 to 6, wherein: The address of the storage resource associated with the second NF is a universal resource locator URL or a fully qualified domain name FQDN.

8. The method according to any one of claims 1 to 7, wherein: The second NF is one of the following: NFp of the communication network or an analysis data repository function ADRF.

9. A method of a producer network function NFp of a communication network, the method comprising: Information associated with a machine learning (ML) model is registered (710) in a network repository function (NRF) of the communication network, wherein: The ML model is generated, owned and / or maintained by the NFp, and The registration information associated with the ML includes an analysis identifier ID and an interoperability ID; and The ML model is encrypted (720) and a first request for storing the encrypted ML model is sent to an analytical data repository function ADRF of the communication network, wherein the first request includes the encrypted ML model or a first address of a storage resource associated with the NFp from which the ML model can be obtained.

10. The method according to claim 9, further comprising: receiving (750) a second request for the ML model from a consumer network function NFc of the communication network, wherein the second request comprises at least one of the analysis ID and the interoperability ID and a first access token; and Based on verifying the first access token, sending (780) a second response to the NFc, the second response including one or more of the following: The ML model, the identifier of the ML model, a first address of a storage resource associated with the NFp, or A second address of a storage resource associated with the ADRF, from which the ML model can be obtained.

11. The method according to claim 10, wherein: The first address of the storage resource associated with the NFp is a first universal resource locator (URL); and The second address of the storage resource associated with the ADRF is a second URL or a fully qualified domain name FQDN.

12. The method according to any one of claims 10 to 11, wherein: The first request includes a first address of a storage resource associated with the NFp; and The second response includes a first address of a storage resource associated with the NFp or a second address of a storage resource associated with the ADRF.

13. The method according to claim 12, wherein: One or more of the following applies: The first address included in the first request is encrypted, and the first request also includes information that can be used to locate a key that can be used to decrypt and verify the first address; and The first address or the second address included in the second response is encrypted, and the second response also includes information that can be used to locate a key that can be used to decrypt and verify the first address or the second address.

14. The method according to any one of claims 12 to 13, further comprising: receiving ( 730 ) another request for the ML model from the ADRF, wherein the another request includes a second access token and a first address of a storage resource associated with the NFp; Based on validating the second access token, sending (735) another response including the encrypted ML model to the ADRF; and A second address of a storage resource associated with the ADRF is then received ( 740 ) from the ADRF.

15. The method according to claim 14, wherein: The registration information associated with the ML model also includes a first address of a storage resource associated with the NFp, and the method further includes updating (745) the registration information associated with the ML model in the NRF to include the received second address.

16. A method according to any one of claims 12 to 15, wherein: The second response includes a first address of a storage resource associated with the NFp; as well as The method further comprises: receiving (790) a third request for the ML model from the NFc, wherein the third request includes: at least one of the analysis ID and the interoperability ID, the first address, and a third access token associated with the ML model; and Based on validating the third access token, a third response including the ML model is sent ( 795 ) to the NFc.

17. The method according to claim 16, wherein: The ML model included in the third response is encrypted, and the third response also includes information that can be used to locate a key that can be used to decrypt and verify the ML model.

18. The method according to any one of claims 10 to 11, further comprising: sending ( 755 ) a fourth request for an access token associated with the ML model to a network repository function (NRF) of the communication network, wherein the fourth request includes at least one of the analysis ID and the interoperability ID; receiving ( 760 ) the requested access token from the NRF; sending ( 765 ) a fifth request for the ML model to the ADRF, wherein the fifth request includes at least one of the analysis ID and the interoperability ID and the received access token; A fifth response is received ( 770 ) from the ADRF including the ML model, which is then included in a second response to the NFc.

19. The method according to any one of claims 9 to 18, wherein: One or more of the following applies: The NFc is an analysis logic function of a network data analysis function NWDAF (AnLF); and The NFp is the model training logic function NWDAF (MTLF) of the network data analysis function.

20. A method of a network repository function (NRF) of a communication network, the method comprising: registering (810) information associated with a machine learning ML model produced, owned and / or maintained by a producer network function NFp of the communication network, wherein the registration information associated with the ML model comprises an analysis identifier ID and an interoperability ID; and receiving ( 830 ) a first request for a first access token associated with the ML model from a consumer network function NFc of the communication network, wherein the first request includes at least one of the analysis ID and the interoperability ID; A first response including the first access token is sent (840) to the NFc.

21. The method according to claim 20, further comprising: A second request for a second access token is received (850) from a first network function NF of the communication network, wherein the second request comprises: at least one of the analysis ID and the interoperability ID; and One of: a first address of a storage resource associated with the NFp, from which the ML model can be obtained; or a second address of a storage resource associated with an analytical data repository function ADRF of the communication network, from which the ML model can be obtained; and A second response including the second access token is sent (860) to the first NF.

22. The method of claim 21, wherein: The first address of the storage resource associated with the NFp is a first universal resource locator (URL); and The second address of the storage resource associated with the ADRF is a second URL or a fully qualified domain name FQDN.

23. The method according to any one of claims 21 to 22, wherein: The first NF is the NFc or the NFp.

24. The method according to any one of claims 21 to 23, wherein: The registration information associated with the ML model also includes a first address of a storage resource associated with the NFp, and the method further includes updating (820) the registration information to include a second identifier of the storage resource associated with the ADRF.

25. The method according to any one of claims 21 to 24, wherein: One or more of the following applies: The NFc is an analysis logic function of a network data analysis function NWDAF (AnLF); and The NFp is the model training logic function NWDAF (MTLF) of the network data analysis function.

26. A method of analyzing a data repository function (ADRF) of a communication network, the method comprising: receiving (910) a first request for storing an encrypted machine learning ML model from a producer network function NFp of the communication network, wherein the first request comprises the encrypted ML model or a first address of a storage resource associated with the NFp from which the encrypted ML model is available; storing (940) the encrypted ML model in a storage resource associated with the ADRF; and A first response is sent (950) to the NFp, the first response including a second address of a storage resource associated with the ADRF.

27. The method of claim 26, wherein: The first request includes a first address of a storage resource associated with the NFp; as well as The method further comprises: sending (920) another request for the ML model to the NFp, wherein the another request includes the first address and a second access token; and Another response is received (930) from the NFp including the encrypted ML model, which is then stored in a storage resource associated with the ADRF.

28. The method of claim 26, further comprising: receiving (960) a second request for the ML model from a first network function NF of the communication network, wherein the second request comprises at least one of the analysis ID and the interoperability ID and a third access token; Based on verifying the third access token, a second response including the ML model is sent (970) to the first NF.

29. The method according to claim 28, wherein: The first NF is an NFp or a consumer NF "NFc" of the communication network.

30. The method of claim 29, wherein: One or more of the following applies: The NFc is an analysis logic function of a network data analysis function NWDAF (AnLF); and The NFp is the model training logic function NWDAF (MTLF) of the network data analysis function.

31. The method according to any one of claims 28 to 30, wherein: The ML model included in the second response is encrypted, and the second response also includes information that can be used to locate a key that can be used to decrypt and verify the ML model.

32. A method according to any one of claims 26 to 31, wherein: The first address of the storage resource associated with the NFp is a first universal resource locator (URL); and The second address of the storage resource associated with the ADRF is a second URL or a fully qualified domain name FQDN.

33. A consumer network function NFc (410, 510, 1008, 1200, 1402) configured to operate in a communication network (198, 200, 1006), wherein: The NFc is implemented by operatively coupled processing circuits (1202, 1404) and communication interface circuits (1206, 1404), and The processing circuit and the communication interface circuit are configured to: Sending a first request for a first access token associated with a machine learning ML model to a first network function NF (420, 440, 520, 540, 1008, 1200, 1402) of the communication network, wherein the first request comprises one or more of the following items associated with the ML model: an analysis identifier ID; and an interoperability ID; receiving, from the first NF, a first response including the first access token; sending a second request for the ML model to a producer NF "NFp" (430, 530, 1008, 1200, 1402) of the communication network, wherein the second request includes at least one of the analysis ID and the interoperability ID and the first access token; and A second response is received from the NFp, the second response comprising one or more of the following: The ML model, the identifier of the ML model, and An address of a storage resource associated with a second NF (430, 440, 530, 540, 1008, 1200, 1402) of the communication network, from which the ML model is obtainable.

34. The NFc according to claim 33, wherein The processing circuit and the communication interface circuit are further configured to perform operations corresponding to the method according to any one of claims 2 to 8.

35. A consumer network function NFc (410, 510, 1008, 1200, 1402) configured to operate in a communication network (198, 200, 1006), the NFc being further configured to: A first request for a first access token associated with a machine learning ML model is sent to a first network function NF (420, 440, 520, 540, 1008, 1200, 1402) of the communication network, wherein The first request includes one or more of the following items associated with the ML model: an analysis identifier ID; and an interoperability ID; receiving, from the first NF, a first response including the first access token; sending a second request for the ML model to a producer NF "NFp" (430, 530, 1008, 1200, 1402) of the communication network, wherein the second request includes at least one of the analysis ID and the interoperability ID and the first access token; and A second response is received from the NFp, the second response comprising one or more of the following: The ML model, the identifier of the ML model, and An address of a storage resource associated with a second NF (430, 440, 530, 540, 1008, 1200, 1402) of the communication network, from which the ML model is obtainable.

36. The NFc according to claim 35, further configured to perform operations corresponding to the method according to any one of claims 2 to 8.

37. A non-transitory computer-readable medium (1204, 1404) storing computer-executable instructions which, when executed by a processing circuit (1202, 1404) associated with a consumer network function NFc (410, 510, 1008, 1200, 1402), configure the NFc to perform operations corresponding to the method according to any one of claims 1 to 8, the NFc (410, 510, 1008, 1200, 1402) being configured to operate in a communication network (198, 200, 1006).

38. A computer program product (1204a, 1404a) comprising computer executable instructions which, when executed by a processing circuit (1202, 1404) associated with a consumer network function NFc (410, 510, 1008, 1200, 1402), configure the NFc to perform operations corresponding to the method according to any one of claims 1 to 8, the NFc (410, 510, 1008, 1200, 1402) being configured to operate in a communication network (198, 200, 1006).

39. A producer network function NFp (430, 530, 1008, 1200, 1402) configured to operate in a communication network (198, 200, 1006), wherein: The NFp is implemented by operatively coupled processing circuits (1202, 1404) and communication interface circuits (1206, 1404), and The processing circuit and the communication interface circuit are configured to: Registering information associated with the machine learning ML model in a network repository function NRF (420, 520, 1008, 1200, 1402) of the communication network, wherein: The ML model is generated, owned and / or maintained by the NFp, and The registration information associated with the ML includes an analysis identifier ID and an interoperability ID; and The ML model is encrypted and a first request for storing the encrypted ML model is sent to an analytical data repository function ADRF (440, 540, 1008, 1200, 1402) of the communication network, wherein the first request includes the encrypted ML model or a first address of a storage resource associated with the NFp, from which the ML model can be obtained.

40. The NFp according to claim 39, wherein The processing circuit and the communication interface circuit are further configured to perform operations corresponding to the method according to any one of claims 10 to 19.

41. A producer network function NFp (430, 530, 1008, 1200, 1402) configured to operate in a communication network (198, 200, 1006), the NFp being further configured to: Registering information associated with the machine learning ML model in a network repository function NRF (420, 520, 1008, 1200, 1402) of the communication network, wherein: The ML model is generated, owned and / or maintained by the NFp, and The registration information associated with the ML includes an analysis identifier ID and an interoperability ID; and The ML model is encrypted and a first request for storing the encrypted ML model is sent to an analytical data repository function ADRF (440, 540, 1008, 1200, 1402) of the communication network, wherein the first request includes the encrypted ML model or a first address of a storage resource associated with the NFp, from which the ML model can be obtained.

42. The NFp according to claim 41, further configured to perform operations corresponding to the method according to any one of claims 10 to 19.

43. A non-transitory computer-readable medium (1204, 1404) storing computer-executable instructions which, when executed by a processing circuit (1202, 1404) associated with a producer network function NFp (430, 530, 1008, 1200, 1402), configure the NFp to perform operations corresponding to the method according to any one of claims 9 to 19, the NFp (430, 530, 1008, 1200, 1402) being configured to operate in a communication network (198, 200, 1006).

44. A computer program product (1204a, 1404a) comprising computer executable instructions which, when executed by a processing circuit (1202, 1404) associated with a producer network function NFp (430, 530, 1008, 1200, 1402), configure the NFp to perform operations corresponding to the method according to any one of claims 9 to 19, the NFp (430, 530, 1008, 1200, 1402) being configured to operate in a communication network (198, 200, 1006).

45. A network repository function (NRF) (420, 520, 1008, 1200, 1402) configured to operate in a communication network (198, 200, 1006), wherein: The NRF is implemented by operatively coupled processing circuits (1202, 1404) and communication interface circuits (1206, 1404), and The processing circuit and the communication interface circuit are configured to: registering information associated with a machine learning ML model produced, owned and / or maintained by a producer network function NFp (430, 530, 1008, 1200, 1402) of the communication network, wherein the registration information associated with the ML model includes an analysis identifier ID and an interoperability ID; and receiving a first request for a first access token associated with the ML model from a consumer network function NFc (410, 510, 1008, 1200, 1402) of the communication network, wherein the first request includes at least one of the analysis ID and the interoperability ID; A first response including the first access token is sent to the NFc.

46. ​​The NRF according to claim 45, wherein The processing circuit and the communication interface circuit are further configured to perform operations corresponding to the method according to any one of claims 21 to 25.

47. A network repository function (NRF) (420, 520, 1008, 1200, 1402) configured to operate in a communication network (198, 200, 1006), the NRF being further configured to: registering information associated with a machine learning ML model produced, owned and / or maintained by a producer network function NFp (430, 530, 1008, 1200, 1402) of the communication network, wherein Registration information associated with the ML model includes an analysis identifier ID and an interoperability ID; and receiving a first request for a first access token associated with the ML model from a consumer network function NFc (410, 510, 1008, 1200, 1402) of the communication network, wherein the first request includes at least one of the analysis ID and the interoperability ID; A first response including the first access token is sent to the NFc.

48. The NRF according to claim 47, further configured to perform operations corresponding to the method according to any one of claims 21 to 25.

49. A non-transitory computer-readable medium (1204, 1404) storing computer-executable instructions which, when executed by a processing circuit (1202, 1404) associated with a network repository function (NRF) (420, 520, 1008, 1200, 1402), configure the NRF to perform operations corresponding to the method according to any one of claims 20 to 25, the NRF (420, 520, 1008, 1200, 1402) being configured to operate in a communication network (198, 200, 1006).

50. A computer program product (1204a, 1404a) comprising computer executable instructions which, when executed by a processing circuit (1202, 1404) associated with a network repository function (NRF) (420, 520, 1008, 1200, 1402), configure the NRF to perform operations corresponding to the method according to any one of claims 20 to 25, the NRF (420, 520, 1008, 1200, 1402) being configured to operate in a communication network (198, 200, 1006).

51. An analytical data repository function (ADRF) (440, 540, 1008, 1200, 1402) configured to operate in a communication network (198, 200, 1006), wherein: The ADRF is implemented by operatively coupled processing circuits (1202, 1404) and communication interface circuits (1206, 1404), and The processing circuit and the communication interface circuit are configured to: receiving a first request for storing an encrypted machine learning ML model from a producer network function NFp (430, 530, 1008, 1200, 1402) of the communication network, wherein the first request comprises the encrypted ML model or a first address of a storage resource associated with the NFp, from which the encrypted ML model is available; storing the encrypted ML model in a storage resource associated with the ADRF; and A first response is sent to the NFp, the first response including a second address of a storage resource associated with the ADRF.

52. The ADRF of claim 51, wherein: The processing circuit and the communication interface circuit are further configured to perform operations corresponding to the method according to any one of claims 27 to 32.

53. An analysis data repository function (ADRF) (440, 540, 1008, 1200, 1402) configured to operate in a communication network (198, 200, 1006), the ADRF further configured to: A first request for storing an encrypted machine learning ML model is received from a producer network function NFp (430, 530, 1008, 1200, 1402) of the communication network, wherein The first request includes the encrypted ML model or a first address of a storage resource associated with the NFp, from which the encrypted ML model can be obtained; storing the encrypted ML model in a storage resource associated with the ADRF; as well as A first response is sent to the NFp, the first response including a second address of a storage resource associated with the ADRF.

54. The ADRF according to claim 53, further configured to perform operations corresponding to the method according to any one of claims 27 to 32.

55. A non-transitory computer-readable medium (1204, 1404) storing computer-executable instructions which, when executed by processing circuitry (1202, 1404) associated with an analysis data repository function (ADRF) (440, 540, 1008, 1200, 1402), configure the ADRF to perform operations corresponding to the method according to any one of claims 26 to 32, the ADRF (440, 540, 1008, 1200, 1402) being configured to operate in a communication network (198, 200, 1006).

56. A computer program product (1204a, 1404a) comprising computer executable instructions which, when executed by a processing circuit (1202, 1404) associated with an analysis data repository function ADRF (440, 540, 1008, 1200, 1402), configure the ADRF to perform operations corresponding to the method according to any one of claims 26 to 32, the ADRF (440, 540, 1008, 1200, 1402) being configured to operate in a communication network (198, 200, 1006).

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