Methods and apparatus for support of vertical federated learning at network data analytics function (NWDAF)

Abstract

Apparatus and methods to perform vertical federated learning (VFL) in a network, e.g. 5G New Radio network. At step 13, a VFL server, such as a Network Data Analytics Function (NWDAF) hosting VFL functionality, receives a message from a service consumer such as a consumer network function (NF). The message may be a subscription to analytics or a request for analytics. At step 14, a distributed VFL inference process is triggered by the VFL server invoking a Machine Learning (ML) model inference request. The request is transmitted to a participant, i.e. a VFL client entity which may also be a NWDAF. At step 15, based on the request, the VFL client entity obtains intermediate inference results by performing local inference computation using a local ML model. At step 16, if the VFL client entity is the last participant, the intermediate inference results are transmitted to the VFL server. At step 17, the VFL server performs further inference computation on the received intermediate inference results and derives the requested analytics which may be delivered to the NF consumer at step 18. The VFL server may discover or select the VFL client entities to participate in the VFL procedure.

Description

BACKGROUND Field

[0001] Certain examples of the present disclosure provide methods and apparatus for supporting vertical federated learning (VFL) at Network Data Analytics Functions (NWDAFs). Description of Related Art

[0002] The content of the following documents is referred to below and / or their content provides background information and context that the following disclosure should be considered in view of: [1] SP-231800, Study on Core Network Enhanced Support for Artificial Intelligence (Al) / Machine Learning (ML), 3GPPTSG SA Meeting #102, 11-15 December 2023, Edinburgh, UK. [2] 3GPP TS 23.288 V18.4.0, Architecture enhancements for 5G System (5GS) to support network data analytics services. [3] 3GPP TR 23.700-84 V0.1.0, Study on Core Network Enhanced Support for Artificial Intelligence (Al) / Machine Learning (ML). (Note: the example versions shown for each TS are non-limiting, other versions of the TS may be considered also)

[0003] Wireless or mobile (cellular) communications networks in which a mobile terminal (e.g., user equipment (UE), such as a mobile handset) communicates via a radio link with a network of base stations, or other wireless access points or nodes, have undergone rapid development through a number of generations. The 3rd Generation Partnership Project (3GPP) design, specify and standardise technologies for mobile wireless communication networks. Fourth Generation (4G) and Fifth Generation (5G) systems are now widely deployed, and development of Sixth Generation (6G) Systems is in progress.

[0004] 3GPP standards for 4G systems include an Evolved Packet Core (EPC) and an Enhanced-UTRAN (E-UTRAN: an Enhanced Universal Terrestrial Radio Access Network). The E-UTRAN uses Long Term Evolution (LTE) radio technology. LTE is commonly used to refer to the whole system including both the EPC and the E-UTRAN, and LTE is used in this sense in the remainder of this document. LTE should also be taken to include LTE enhancements such as LTE Advanced and LTE Pro, which offer enhanced data rates compared to LTE.

[0005] In 5G systems a new air interface has been developed, which may be referred to as 5G New Radio (5G NR) or simply NR. NR is designed to support the wide variety of services and use case scenarios envisaged for 5G networks, though builds upon established LTE technologies. New frameworks and architectures are also being developed as part of 5G networks in order to increase the range of functionality and use cases available through 5G networks.

[0006] 3GPP has also started studying the benefits of introducing Artificial Intelligence (Al) / Machine Learning (ML) solutions to communications networks, for example, enhancement of management and orchestration, performance, resource allocation, in addition to reduction of complexity and overhead in the network

[0007] In AI / ML operation, AI / ML models and / or data might be transferred across the AI / ML applications (AFs), 5GC and UEs. The AI / ML works could be divided into two main phases: model training and inference. During model training and inference, multiple rounds of interaction may be required.

[0008] From the perspective of the operation types, AI / ML operation types may be categorised into three types: model splitting, model sharing, and distributed / federated learning. Federated Learning Support at NWDAF

[0009] In current SA2 specifications, the Federated Learning (FL) among multiple NWDAFs (so called Horizontal Federated Learning) were supported since 3GPP Rel-18. High-level and detailed descriptions of supporting Federated Learning (FL) among multiple NWDAFs are mainly documented in clause 5.3 and clause 6.2c of TS 23.288 [2],

[0010] In Rei-16, a single instance or multiple instances of NWDAF may be deployed in a Public land mobile network (PLMN). In case multiple NWDAF instances are deployed, the architecture supports deploying the NWDAF as a central NF, as a collection of distributed NFs, or as a combination of both. When multiple NWDAFs exist, not all of them need to be able to provide the same type of analytics results. However, no specific requirement has been defined regarding how different NWDAFs could cooperate in Rel-16. In Rel-16, each NWDAF acts independently from the other NWDAFs.

[0011] In reality, some of the NWDAFs in one network may be providing the same type of analytics, and so may help each other for e.g. specific analytics for specific target UEs or specific analytics for specific area of interest. Although some of these NWDAFs may be providing different type of analytics, they may still be able to help each other if e.g. analytics are somehow related: one example is for expected UE behavioural parameters related network data analytics, which have a tight relation with UE mobility analytics and UE communication analytics. In another example, in order to build abnormal behaviour related network data analytics, the NWDAF would need to collect similar type of data to the data needed to build analytics for UE mobility pattern and for UE communication pattern.

[0012] In order to address the coordination among multiple NWDAFs for Federated Learning (FL), Rel-18 study and normative work were carried out by SA2. As documented in clause 5.3ofTS 23.288 [2]: Federated learning among multiple NWDAFs is a machine learning technique in core network that trains an ML Model across multiple decentralized entities holding local data set, without exchanging / sharing local data set. This approach stands in contrast to traditional centralized machine learning techniques where all the local datasets are uploaded to one server, thus allowing to address critical issues such as data privacy, data security, data access rights. For Federated Learning supported by multiple NWDAFs containing MTLF, there is one NWDAF containing MTLF acting as FL server (called FL server NWDAF for short) and multiple NWDAFs containing MTLF acting as FL client (called FL client NWDAF for short).

[0013] The FL server NWDAF and FL client NWDAF have different functionalities (in clause 5.3 of TS 23.288 [2]): FL server NWDAF: discovers and selects FL client NWDAFs to participant in an FL procedure requests FL client NWDAFs to do local model training and to report local model information. generates global ML model by aggregating local model information from FL client NWDAFs. sends the global ML model back to FL client NWDAFs and repeats training iteration if needed. FL client NWDAF: locally trains ML model that tasked by the FL server NWDAF with the available local data set, which includes the data that is not allowed to share with others due to e.g. data privacy, data security, data access rights. - reports the trained local ML model information to the FL server NWDAF. receives the global ML model feedback from FL server NWDAF and repeats training iteration if needed.

[0014] Either the NWDAF containing MTLF or the NWDAF containing AnLF can trigger the ML model training, as a consumer. The NWDAF containing MTLF determines to train an ML model either based on local configuration or when it receives the request from NWDAF containing AnLF. The NWDAF containing MTLF may further determine whether the ML model should be trained via FL mechanism based on different aspects, e.g. Analytic ID, Service Area / DNAI or data cannot be obtained directly from data producer NF (e.g. due to data privacy, data security). However, the NWDAF containing AnLF is not aware whether the ML model is trained based on FL or not.

[0015] In order to performance the FL among multiple NWDAFs, before FL procedure is initiated, appropriate NWDAFs containing MTLF that can act as an FL server and FL clients should be discovered and chosen based on specified criteria and interactions between the FL server and FL clients.

[0016] When starting an FL procedure, the FL server NWDAF provides an initial model to each FL client NWDAFs, and then each FL client NWDAFs perform local model training using their local data set based on the request from FL server.

[0017] During the FL execution phase, in order to maintain a Federation Learning process, considering the performance and capability of FL Client NWDAF(s), the FL Server NWDAF may trigger reselection, addition, or removal of FL Client NWDAF(s), discovers new FL Client NWDAF(s) via NRF and FL Client NWDAF(s) joins or leaves Federated Learning process dynamically.

[0018] The detailed procedures of Registration and Discovery procedure for Federated Learning, General procedure for Federated Learning among Multiple NWDAF Instances, Procedures for Maintaining Federated Learning Processes are documented in clause 6.2C.2.1, 6.2C.2.2 and 6.2C.2.3 of TS 23.288 [2], General procedure for Federated Learning among Multiple NWDAF Instances

[0019] A General procedure for Federated Learning among Multiple NWDAF is shown in Figure 1, as documented in clause 6.2C.2.2 of TS 23.288 [2]:

[0020] 0. The consumer (NWDAF containing AnLF or NWDAF containing MTLF) sends a subscription request to FL server NWDAF to retrieve an ML model, using Nnwdaf_MLModelProvision service as defined in clause 7.5 including Analytics ID, ML model metric (e.g., ML model Accuracy), Accuracy reporting interval, pre-determined status (ML model Accuracy threshold or Time when the ML model is needed).

[0021] If the consumer (i.e. the NWDAF containing AnLF or NWDAF containing MTLF) provides the Time when the ML model is needed, the FL Server NWDAF can take this information into account to decide the maximum response time for its FL Client NWDAF(s).

[0022] 1. FL Server NWDAF selects NWDAF(s) containing MTLF (FL Client NWDAF(s)) as described in clause 6.2C.2.1.

[0023] 2. FL Server NWDAF sends a Nnwdaf_MLModelTraining_Subscribe or Nnwdaf_MLModelTraininglnfo_Request to the selected NWDAF containing MTLF (FL Client NWDAF(s)), which participates in the Federated learning to perform the local model training and determine the interim local ML model information based on the input parameter in the request from FL Server NWDAF. The request includes ML model metric and initial ML model and also includes the maximum response time, the FL Client NWDAF has to report the interim local ML model information to the FL Server NWDAF before the maximum response time elapses.

[0024] 3. [Optional] Each FL Client NWDAF collects its local data by using the current mechanism in clause 6.2 if the Client NWDAF has not local data available already.

[0025] 4. During Federated Learning training procedure, each FL Client NWDAF further trains the ML model provided by the FL Server NWDAF based on its own data and reports the interim local ML model information to the FL Server NWDAF in Nnwdaf_MLModelTraining_Notify or Nnwdaf_MLModelTraininglnfo_Request response. The Nnwdaf_MLModelTraining_Notify or Nnwdaf_MLModelTraininglnfo_Request response may also include the Status report of FL training that includes local ML model metric computed by the FL Client NWDAF and Training Input Data Information (e.g. areas covered by the data set, sampling ratio, maximum / minimum of value of each dimension of data, etc.) in the FL Client NWDAF. The Nnwdaf_MLModelTraining_Notify or Nnwdaf_MLModelTraininglnfo_Response also includes the global ML Model Accuracy when the ML Model Accuracy Check Flag was included in the Nnwdaf_MLModelTraining_Subscribe or Nnwdaf_MLModelTraininglnfo_Request (as described in step 7), the global ML Model Accuracy is calculated by the FL Client NWDAF using the local training data as the testing dataset.

[0026] The local ML model, which is sent from the FL Client NWDAF(s) to the FL Server NWDAF during the FL training process, is the information needed by the FL Server NWDAF to build the aggregated model.

[0027] If the FL Client NWDAF is not able to complete the training of the interim local ML model within the maximum response time provided by the FL Server NWDAF, the FL Client NWDAF shall send the Delay Event Notification that include the delay event indication, an optional cause code (e.g. local ML model training failure, more time necessary for local ML model training) and the expected time to complete the training if available to the FL Server NWDAF before the maximum response time elapses.

[0028] 4a. [Optional]lf FL Server NWDAF receives notification / response that the FL Client NWDAF is not able to complete the training within the maximum response time, the FL Server NWDAF may send to the FL Client NWDAF a new maximum response time in Nnwdaf_MLModelTraining_Subscribe or Nnwdaf_MLModelTraininglnfo_Request, before which the FL Client NWDAF has to report the interim local ML model information to the FL Server NWDAF. Otherwise, the FL Server NWDAF may indicate FL Client NWDAF to skip reporting for this iteration. FL Server NWDAF includes the current iteration round ID in the message to indicate that the request is to modify the training parameters of the current iteration round.

[0029] Alternatively, the FL Server NWDAF may inform the FL Client NWDAF to cease the ML model training by sending termination request and to report back the current local ML model updates.

[0030] 5. The FL Server NWDAF aggregates all the local ML model information retrieved at step 4, to update the global ML model. The FL Server NWDAF may also compute the global ML model metric, e.g. based on the local ML model metric(s) or by applying the global model on the validation dataset (if available). The FL Server NWDAF may update the global ML model each time a FL Client NWDAF provides updated local ML model information, or the FL Server NWDAF may decide to wait for local ML model information from all FL Client NWDAFs before updating the global ML model.

[0031] If the FL Server NWDAF provides the maximum response time for the FL Client NWDAF(s) to provide the interim local ML model information in step 2, or the new maximum response time in step 4a, the FL Server NWDAF decides either to wait for the FL Client NWDAF(s) which have not yet provided their interim local ML model within the new maximum response time or to aggregate only the retrieved local ML model information instances to update global ML model. The FL Server NWDAF makes this decision, considering the notification / response from the FL Client NWDAF or, if the notification is not received, based on local configuration.

[0032] 6a. [Optional] Based on the consumer request in step 0, the FL Server NWDAF sends a Nnwdaf_MLModelProvision_Notify message to update the ML model metric to the consumer periodically (e.g. a certain number of training rounds or every 10 min) or dynamically when some pre-determined status is achieved (e.g. the ML Model Accuracy threshold is achieved or training time expires).

[0033] 6b. [Optional] The consumer decides whether the current model can fulfil the requirement, e.g. global ML model metric is satisfactory for the consumer and determines to stop or continue the training process. The consumer re-invokes Nnwdaf_MLModelProvision_Subscribe service operation as used in step 0 to continue the training process or invokes Nnwdaf_MLModelProvision_Unsubscribe service operation to stop the training process.

[0034] 6c. [Optional] Based on the subscription request sent from the consumer in step 6b, the FL Server NWDAF updates or terminates the current FL training process.

[0035] If the FL Server NWDAF received a request in step 6b to stop the Federated Training process, steps 7 and 8 are skipped.

[0036] 7. If the FL procedure continues, FL Server NWDAF may determine FL Client NWDAF as described in clause 6.2C.2.3 and sends Nnwdaf_MLModelTraining_Subscribe or Nnwdaf_MLModelTraininglnfo_Request that includes the aggregated ML model information to selected FL Client NWDAF(s) for next round of Federated Training. The request may also include the ML Model Accuracy Check Flag, that indicates the FL Client NWDAF(s) to use the local training data as the testing dataset to calculate the Model Accuracy of the global ML model provided by the FL Server NWDAF.

[0037] 8. Each FL Client NWDAF updates its own ML model based on the aggregated ML model information distributed by the FL Server NWDAF at step 7.

[0038] When the Federated Training procedure is complete, the FL Server NWDAF requests the FL client NWDAF(s) to terminate the FL procedure by invoking Nnwdaf_MLModelTraining_Unsubscribe service with a cause code that the FL process has finished and optionally with the final aggregated ML model information. Then the FL client NWDAF(s) terminate the local model training and if the final aggregated ML model information is received from the FL server NWDAF, the FL client NWDAF(s) can store it for further use.

[0039] 9. After the training process is complete, the FL Server NWDAF may send Nnwdaf_MLModelProvision_Notify that includes the globally optimal ML model information to the consumer. SUMMARY

[0040] In the rapidly evolving landscape of telecommunications, future 6G networks promise an era of unprecedented connectivity, supporting a wide array of devices and applications, from smartphones to Internet of Things (loT) devices, and autonomous vehicles. This technological leap forward come with a surge in data volume, variety, and velocity, presenting both opportunities and challenges in data management, privacy, and utilization. Central to harnessing the potential of this data is the ability to analyze and derive actionable insights in real-time, across diverse network environments and geographies.

[0041] The approaches of the present disclosure address these challenges by introducing an innovative approach to data analytics and machine learning model training within the context of Vertical Federated Learning (VFL) across multiple network entities. This approach is designed to optimize network performance, enhance user experience, and maintain / support stringent data privacy standards, all within the framework established by the 3rd Generation Partnership Project (3GPP) for networks and services.

[0042] More specifically, the present disclosure provides a novel method and system for implementing VFL among distributed network entities, enabling collaborative, privacypreserving machine learning without or with a reduced need for direct data sharing. This is achieved through a sophisticated orchestration of data processing, model training, and optimization processes that leverage the unique capabilities of network entities / components across different network segments and service areas. By doing so, the proposed approach facilitates a more efficient and effective utilization of network data, leading to improved predictive models and analytics that are valuable for network management, service quality assurance, and the development of new services.

[0043] An important aspect of the approaches of the present disclosure is the introduction of advanced mechanisms for data alignment, model parameter optimization, and iterative learning, which collectively enhance the accuracy and reliability of the federated models developed through this process. Furthermore, the disclosed approaches outline a structured procedure for the participation of network entities in the VFL process, including registration, model request and provisioning, participant selection, data alignment and processing, as well as model evaluation and feedback mechanisms. These procedures help to ensure that each participating entity can contribute to and benefit from the federated learning process, despite the inherent challenges of distributed data and the need for privacy preservation.

[0044] According to an aspect of the present disclosure, there is provided a server entity for performing a vertical federated learning (VFL) procedure, the server entity configured to: based on a message received from a service consumer, initiate the VFL procedure; transmit a request for VFL inference to at least one VFL client entity; receive intermediate inference results generated by the at least one VFL client entity; and perform inference computation on the received intermediate inference results. In various examples, the request for VFL inference is a request associated with VFL inference.

[0045] According to various examples, the server entity is further configured to discover or select the at least one VFL client entity to participate in the VFL procedure.

[0046] According to various examples, the server entity is further configured to: derive analytics according to the message, based on the inference computation; and transmit the derived analytics to the service consumer.

[0047] According to various examples, the message is an analytics request received from the service consumer.

[0048] According to various examples, the server entity is further configured to register, to network repository function (NRF), information including at least one of network function (NF) profile, analytics ID(s), service area, VFL capability information, and time interval supporting VFL.

[0049] According to various examples, the server entity is further configured to transmit a VFL preparation request to one or more VFL client, the VFL preparation request including at least one of analytics ID, ML model interoperability information, available data requirement, availability time requirement, or information for checking whether a ML model training requirement can be met.

[0050] According to various examples, the server entity is further configured to transmit dataset identifiers to the one or more VFL client entity.

[0051] According to various examples, the server entity is further configured to: receive, from each of the one or more VFL client entity, information including an indication of whether said VFL client entity will participate; and select the at least one VFL client entity from among the one or more VFL client entity based on the received information.

[0052] According to various examples, the received information further comprises a reason why said VFL client entity cannot participate.

[0053] According to various examples, the server entity is further configured to: transmit, to the at least one VFL client entity, a request to perform ML model training; and receive, from the at least one VFL client entity, intermediate training results.

[0054] According to various examples, the server entity is further configured to perform VFL computation based on the received intermediate training results.

[0055] According to various examples, the intermediate training results relate to a ML model associated with the VFL inference.

[0056] According to another aspect of the present disclosure there is provided a vertical federated learning (VFL) client entity for participating in a VFL procedure, the VFL client entity configured to: receive a request for VFL inference from a server entity; based on the request, obtain intermediate inference results using a local machine learning (ML) model; and transmit the intermediate inference results to the server entity. Transmitting the intermediate inference results may be conditional on the VFL client entity being the last participant (e.g. out of a number of VFL client entities) or a particular participant among a plurality of participants. In various examples, the operation of transmitting the intermediate results to the server entity is replaced with an operation of transmitting the intermediate inference results to another VFL client entity. In various examples, the request for VFL inference is a request associated with VFL inference.

[0057] According to various examples, the VFL client entity is further configured to share the intermediate inference results with another VFL client entity.

[0058] According to various examples, the VFL client entity is further configured to register, to network repository function (NRF), information including at least one of network function (NF) profile, analytics ID(s), service area, VFL capability information, and time interval supporting VFL.

[0059] According to various examples, the VFL client entity is further configured to receive a VFL preparation request from the server entity, the VFL preparation request including at least one of analytics ID, ML model interoperability information, available data requirement, availability time requirement, or information for checking whether a ML model training requirement can be met.

[0060] According to various examples, the VFL client entity is further configured to: based on the VFL preparation request, determine capability to meet model training requirements; and transmit, to the server entity, information including an indication of whether the VFL client entity will participate in the VFL procedure.

[0061] According to various examples, the VFL client entity is further configured to: receive dataset identifier from the server entity.

[0062] According to various examples, the transmitted information further includes a reason why the VFL client entity cannot participate.

[0063] According to various examples, the VFL client entity is further configured to: receive, from the server entity, a request to perform ML model training; and train the local ML model; wherein the trained local ML model is used to obtain the intermediate inference results.

[0064] According to various examples, the VFL client entity is further configured to: obtain intermediate training results of the trained local ML model, and share the intermediate training results with the server entity; or refine a previously trained model to obtain the trained local ML model and share the model refinement results with the server entity.

[0065] According to various examples, the intermediate training results or the model refinement results are shared using a same ML model training service as used for receiving the request to perform ML model training.

[0066] According to various examples, the VFL client entity is further configured to share the intermediate training results with another VFL client entity.

[0067] According to various examples, the VFL client entity is further configured to register, to network repository function (NRF), information including at least one of network function (NF) profile, analytics ID(s), service area, VFL capability information, and time interval supporting VFL.

[0068] According to various examples, the VFL client entity is further configured to receive a VFL preparation request from the server entity, the VFL preparation request including at least one of analytics ID, ML model interoperability information, available data requirement, availability time requirement, or information for checking whether a ML model training requirement can be met.

[0069] According to various examples, the VFL client entity is further configured to: based on the VFL preparation request, determine capability to meet model training requirements; and transmit, to the server entity, information including an indication of whether the VFL client entity will participate in the VFL procedure.

[0070] According to various examples, the VFL client entity is further configured to receive dataset identifier from the server entity.

[0071] According to various examples, the transmitted information further includes a reason why the VFL client entity cannot participate.

[0072] According to various examples, the VFL client entity is further configured to: receive, from the server entity, a request to perform ML model training; and train the local ML model; wherein the trained local ML model is used to obtain the intermediate inference results.

[0073] According to various examples, the VFL client entity is further configured to: obtain intermediate training results of the trained local ML model, and share the intermediate training results with the server entity; or refine a previously trained model to obtain the trained local ML model and share the model refinement results with the server entity.

[0074] According to various examples, the intermediate training results or the model refinement results are shared using a same ML model training service as used for receiving the request to perform ML model training.

[0075] According to various examples, the VFL client entity is further configured to share the intermediate training results with another VFL client entity.

[0076] According to another aspect of the present disclosure there is provided a server entity for performing a vertical federated learning (VFL) procedure, the server entity configured to: transmit a VFL preparation request to one or more VFL client, the VFL preparation request including at least one of analytics ID, ML model interoperability information, available data requirement, availability time requirement, or information for checking whether a ML model training requirement can be met; receive, from each of the one or more VFL client entity, information including an indication of whether said VFL client entity will participate; and select at least one VFL client entity to participate from among the one or more VFL client entity based on the received information.

[0077] According to various examples, the at least one VFL client entity is selected to participate in VFL training and / or VFL inference.

[0078] According to various examples, the server entity is further configured to transmit dataset identifier to the one or more VFL client entity.

[0079] According to various examples, the received information further comprises a reason why said VFL client entity cannot participate.

[0080] According to another aspect of the present disclosure there is provided a server entity for performing a vertical federated learning (VFL) procedure, the server entity configured to: transmit, to at least one VFL client entity, a request to perform machine learning (ML) model training; receive, from the at least one VFL client entity, intermediate training results or model refinement results; and perform VFL computation based on the received results.

[0081] According to various examples, the intermediate training results relate to a ML model associated with a VFL inference process.

[0082] According to another aspect of the present disclosure there is provided a vertical federated learning (VFL) client entity for participating in a VFL procedure, the VFL client entity configured to: receive a VFL preparation request from a server entity, the VFL preparation request including at least one of analytics ID, ML model interoperability information, available data requirement, availability time requirement, or information for checking whether a ML model training requirement can be met; based on the VFL preparation request, determine capability to meet model training requirements; and transmit, to the server entity, information including an indication of whether the VFL client entity will participate in the VFL procedure.

[0083] According to various examples, the model training requirements relate to VFL training and / or VFL inference.

[0084] According to various examples, the transmitted information further includes a reason why the VFL client entity cannot participate.

[0085] According to various examples, the VFL client entity is further configured to receive dataset identifier from the server entity.

[0086] According to another aspect of the present disclosure, there is provided a vertical federated learning (VFL) client entity for participating in a VFL procedure, the VFL client entity configured to: receive, from a server entity, a request to perform machine learning (ML) model training; and train a local ML model; wherein training the local ML model comprises obtaining intermediate training results of the trained local ML model or refining a previously trained model to obtain the trained local ML model; and wherein VFL client entity is configured to share the intermediate training results or the model refinement results with the server entity.

[0087] According to another aspect of the present disclosure, there is provided a server entity for performing a vertical federated learning (VFL) procedure, the server entity configured to: register, to network repository function (NRF), information including at least one of network function (NF) profile, analytics ID(s), service area, VFL capability information, and time interval supporting VFL; and discover one or more VFL client entity via the NRF by invoking a discovery request service operation.

[0088] According to another aspect of the present disclosure, there is provided a vertical federated learning (VFL) client entity for participating in a VFL procedure, the VFL client entity configured to: register, to network repository function (NRF), information including at least one of network function (NF) profile, analytics ID(s), service area, VFL capability information, and time interval supporting VFL.

[0089] According to another aspect of the present disclosure, there is provided a method of a server entity for performing a vertical federated learning (VFL) procedure, the method comprising: based on a message received from a service consumer, initiating the VFL procedure; transmitting a request for VFL inference to at least one VFL client entity; receiving intermediate inference results generated by the at least one VFL client entity; and performing inference computation on the received intermediate inference results.

[0090] According to various examples, the method is modified to be in accordance with any one or more of the examples relating to the server entity given above.

[0091] According to another aspect of the present disclosure, there is provided a method of a vertical federated learning (VFL) client entity for participating in a VFL procedure, the method comprising: receiving a request for VFL inference from a server entity; based on the request, obtaining intermediate inference results using a local machine learning (ML) model; and transmitting the intermediate inference results to the server entity.

[0092] According to various examples, the method is modified to be in accordance with any one or more of the examples relating to the VFL client entity given above.

[0093] According to another aspect of the present disclosure, there is provided a method of a network exposure function (NEF) entity, the method comprising: receiving, from a server entity, a request for vertical federated learning (VFL) inference to be forwarded to at least one VFL client entity; forwarding the request for VFL inference to the at least one VFL client entity; receiving, from the at least one VFL client entity, intermediate inference results generated by the at least one VFL client entity; and forwarding the intermediate interference results to the server entity.

[0094] According to various examples, the method is modified to be in accordance with any one or more of the examples relating to the NEF entity given above.

[0095] According to another aspect of the present disclosure, there is provided a method of a server entity for performing a vertical federated learning (VFL) procedure, the method comprising: transmitting a VFL preparation request to one or more VFL client, the VFL preparation request including at least one of analytics ID, ML model interoperability information, available data requirement, availability time requirement, or information for checking whether a ML model training requirement can be met; receiving, from each of the one or more VFL client entity, information including an indication of whether said VFL client entity will participate; and selecting at least one VFL client entity to participate from among the one or more VFL client entity based on the received information.

[0096] According to various examples, the method is modified to be in accordance with any one or more of the examples relating to the server entity given above.

[0097] According to another aspect of the present disclosure, there is provided a method of a vertical federated learning (VFL) client entity for participating in a VFL procedure, the method comprising: receiving a VFL preparation request from a server entity, the VFL preparation request including at least one of analytics ID, ML model interoperability information, available data requirement, availability time requirement, or information for checking whether a ML model training requirement can be met; based on the VFL preparation request, determining capability to meet model training requirements; and transmitting, to the server entity, information including an indication of whether the VFL client entity will participate in the VFL procedure.

[0098] According to various examples, the method is modified to be in accordance with any one or more of the examples relating to the VFL client entity given above.

[0099] According to another aspect of the present disclosure, there is provided a method of a server entity for performing a vertical federated learning (VFL) procedure, the method comprising: transmitting, to at least one VFL client entity, a request to perform machine learning (ML) model training; receiving, from the at least one VFL client entity, intermediate training results or model refinement results; and performing VFL computation based on the received results.

[00100] According to various examples, the method is modified to be in accordance with any one or more of the examples relating to the server entity given above.

[00101] According to another aspect of the present disclosure, there is provided a method of a vertical federated learning (VFL) client entity for participating in a VFL procedure, the method comprising: receiving, from a server entity, a request to perform machine learning (ML) model training; and training a local ML model; wherein training the local ML model comprises obtaining intermediate training results of the trained local ML model or refining a previously trained model to obtain the trained local ML model; and wherein the method comprises sharing the intermediate training results or the model refinement results with the server entity.

[00102] According to various examples, the method is modified to be in accordance with any one or more of the examples relating to the VFL client entity given above.

[00103] Examples of the present disclosure also include computer-readable storage medium configured to store instructions which, when executed by at least one processor, an apparatus or a computer, cause said at least one processor, apparatus or computer to perform a method according to any one of the aspects of examples described / indicated above.

[00104] It is an aim of certain examples of the present disclosure to address, solve and / or mitigate, at least partly, at least one of the problems and / or disadvantages associated with the related art, for example at least one of the problems and / or disadvantages described herein. It is an aim of certain examples of the present disclosure to provide at least one advantage over the related art, for example at least one of the advantages described herein.

[00105] Other aspects, advantages, and salient features of the disclosure will become apparent to those skilled in the art from the following detailed description taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[00106] Embodiments / examples of the present disclosure are further described hereinafter with reference to the accompanying drawings, in which: Figure 1a provides an illustration of the general procedure for Federated Learning among Multiple NWDAF in TS 23.288 [2] Figure 2 provides a procedure for the support of Vertical Federated Learning (VFL) at NWDAF in accordance with an approach of the present disclosure; Figure 3 provides a procedure for VFL with NWDAF and Application Function (AF) as Participants; and Figure 4 provides a block diagram of an exemplary network entity / function that may be used in certain examples of the present disclosure. Figure 5 is a flow diagram illustrating a method in accordance with an example of the present disclosure. Figure 6 is a flow diagram illustrating a method in accordance with an example of the present disclosure. DETAILED DESCRIPTION

[00107] The following description of examples of the present disclosure, with reference to the accompanying drawings, is provided to assist in a comprehensive understanding of certain examples of the present disclosure. The description includes various specific details to assist in that understanding but these are to be regarded as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the examples described herein can be made without departing from the scope of the disclosure.

[00108] The same or similar components may be designated by the same or similar reference numerals, although they may be illustrated in different drawings.

[00109] Detailed descriptions of techniques, structures, constructions, functions or processes known in the art may be omitted for clarity and conciseness, and to avoid obscuring the subject matter of the present disclosure.

[00110] The terms and words used herein are not limited to the bibliographical or standard meanings, but are merely used to enable a clear and consistent understanding of the disclosure.

[00111] Throughout the description of this specification, the words “comprise”, “include” and “contain” and variations of the words, for example “comprising” and “comprises”, means “including but not limited to”, and is not intended to (and does not) exclude other features, elements, components, integers, steps, processes, operations, functions, characteristics, properties and / or groups thereof.

[00112] Throughout the description of this specification, the singular form, for example “a”, “an” and “the”, encompasses the plural unless the context otherwise requires. For example, reference to “an object” includes reference to one or more of such objects.

[00113] Throughout the description, the expression “at least one of A, B and / or C” (or the like) and the expression “one or more of A, B and / or C” (or the like) should be seen to separately include all possible combinations, for example: A, B, C, A and B, A and C, A and B and C.

[00114] Throughout the description of this specification, language in the general form of “X for Y” (where Y is some action, process, operation, function, activity or step and X is some means for carrying out that action, process, operation, function, activity or step) encompasses means X adapted, configured or arranged specifically, but not necessarily exclusively, to do Y.

[00115] Features, elements, components, integers, steps, processes, operations, functions, characteristics, properties and / or groups thereof described or disclosed in conjunction with a particular aspect, embodiment or example are to be understood to be applicable to any other aspect, embodiment or example described herein unless incompatible therewith.

[00116] The following examples are applicable to, and use terminology associated with, 3GPP 4G (e.g., LTE) and / or 5G (e.g., NR). However, the skilled person will appreciate that the techniques disclosed herein are not limited to these examples or to 3GPP 4G (e.g., LTE) and / or 5G (e.g., NR), and may be applied in any suitable system or standard, for example one or more existing and / or future generation wireless communication systems or standards (e.g., B5G, 5G-Advanced, 6G etc.). The skilled person will appreciate that the techniques disclosed herein may be applied in any existing or future releases of 3GPP 4G (e.g., LTE) and / or 5G (e.g., NR) and / or 5G Advanced and / or 6G, and / or (3GPP Release 17, 18, 19, 20, etc.) or any other relevant standard. For example, the functionality of the various network entities and other features disclosed herein may be applied to corresponding or equivalent entities or features in other communication systems or standards. Corresponding or equivalent entities or features may be regarded as entities or features that perform the same or similar role, function, operation or purpose within the network.

[00117] Furthermore, the following also applies to the present disclosure: • The terms functionality / use-case / configuration / scenario / site may be used interchangeably. • The terms model and model functionality may be used interchangeably. • This disclosure also apply to non-3GPP entities. • The concepts, proposals, solutions, methods, embodiments, figures, and / or examples, presented in this disclosure, would apply to various type of communication systems, such as 4G, 4G-Advanced, 5G, 5G-Advanced, and 6G. Moreover, the above may also apply (in full or part or modified) to systems of Non-Terrestrial Networks (NR-NTN and / or loT-NTN and / or UAV, etc.), in addition to Terrestrial Networks (TN).

[00118] A particular network entity may be implemented as a network element on dedicated hardware, as a software instance running on a dedicated hardware, and / or as a virtualised function instantiated on an appropriate platform, e.g. on a cloud infrastructure.

[00119] The skilled person will appreciate that the present disclosure is not limited to the specific examples disclosed herein. For example: • The techniques disclosed herein are not limited to 3GPP 4G or 5G or 5G-Advanced and also apply to B5G and 6G systems. • One or more entities in the examples disclosed herein may be replaced with one or more alternative entities performing equivalent or corresponding functions, processes or operations. • One or more of the messages in the examples disclosed herein may be replaced with one or more alternative messages, signals or other type of information carriers that communicate equivalent or corresponding information. • One or more further elements, entities and / or messages may be added to the examples disclosed herein. • One or more non-essential elements, entities and / or messages may be omitted in certain examples. • The functions, processes or operations of a particular entity in one example may be divided between two or more separate entities in an alternative example. • The functions, processes or operations of two or more separate entities in one example may be performed by a single entity in an alternative example. • Information carried by a particular message in one example may be carried by two or more separate messages in an alternative example. • Information carried by two or more separate messages in one example may be carried by a single message in an alternative example. • The order in which operations are performed may be modified, if possible, in alternative examples. • The transmission of information between network entities is not limited to the specific form, type and / or order of messages described in relation to the examples disclosed herein.

[00120] Certain examples of the present disclosure may be provided in the form of an apparatus / device / network entity configured to perform one or more defined network functions and / or a method therefor. Such an apparatus / device / network entity may comprise one or more elements, for example one or more of receivers, transmitters, transceivers, processors, controllers, modules, units, and the like, each element configured to perform one or more corresponding processes, operations and / or method steps for implementing the techniques described herein. For example, an operation / function of X may be performed by a module configured to perform X (or an X-module). Certain examples of the present disclosure may be provided in the form of a system (e.g., a network) comprising one or more such apparatuses / devices / network entities, and / or a method therefor. Vertical Federated Learning (VFL)

[00121] Unlike traditional centralized learning approaches, where data is pooled together in a single location, or Horizontal Federated Learning (HFL), where different entities contribute similar types of data about different samples, VFL allows for the collaborative training of machine learning models across entities that hold different types of information about the same entities or events. For example, collaboration is not only limited to a single type of entity e.g. only NWDAFs but instead may make use of dissimilar entities that performed different functions and which may be at different levels / layer of the network, for example, a UE, a NWDAF and a an AMF. This approach is particularly relevant and beneficial for telecommunication networks for several reasons.

[00122] VFL involves multiple parties collaboratively learning a model while keeping their data localized, only exchanging model intermediate results or gradients / learning parameters, not the raw data itself. Each participant in the VFL process contributes a different set of features about the same sample set, enhancing the model's learning capacity without compromising data privacy. This method is especially useful in scenarios where data cannot be shared freely due to privacy concerns, regulatory restrictions, or commercial competitiveness.

[00123] The process of VFL involves several steps such as those below, but is not only limited to these:

[00124] Data Alignment: To start the training, all parties should be referencing the same set of samples. This alignment is typically achieved through Private Set Intersection (PSI), a technique that identifies common samples across datasets without disclosing any actual data.

[00125] Feature Encoding: Parties locally encode their features to maintain data privacy throughout the training process. Techniques such as homomorphic encryption are employed to ensure that data remains secure, even when being processed.

[00126] Local Model Initialization: Each participant initializes a local model tailored to its unique set of features. These models can vary across parties, reflecting the diverse data each holds.

[00127] Collaborative Training: 1. Insight Synthesis Sharing: In this phase, each party processes its data to generate insights or intermediate representations based on its local model and encrypted data. These insights are then shared with the next party in the VFL chain, serving as inputs for further processing. This iterative process continues until the last party integrates all insights to compute an overall outcome or loss, reflecting the collective learning from all parties. 2. Model Refinement: Following the synthesis of insights, the process of model refinement begins. Here, adjustments or gradients are calculated to improve the model based on the collective outcome. These adjustments are securely passed back through the chain, allowing each party to refine its local model parameters. This secure aggregation of adjustments ensures that the model evolves to more accurately represent the data without compromising the privacy of any party's information.

[00128] The usefulness of VFL for communications networks comes from:

[00129] Enhanced Privacy and Security: Telecommunication networks handle vast amounts of sensitive user data that are subject to strict privacy regulations and standards. VFL enables the utilization of this data for network optimization and service improvement without exposing individual user data, thus maintaining privacy and security.

[00130] Richer Insights from Diverse Data Sources: Telecommunication networks are inherently complex, with data collected from a wide range of sources, including user devices, network equipment, and service platforms. VFL allows for the integration of diverse data types across these sources, leading to richer insights and more accurate network analytics to, for example, predict network demand, detect anomalies, or enhancing user experience, among others.

[00131] Operational Efficiency: By enabling collaborative model training across different network entities and domains without centralizing data, VFL can significantly reduce the bandwidth and storage requirements typically associated with large-scale data analytics. This efficiency is especially useful for real-time or near-real-time applications such as dynamic network slicing, congestion management, and service quality optimization.

[00132] Cross-Vendor Collaboration: The telecommunication industry often involves multiple vendors and operators working within the same ecosystem. VFL facilitates collaboration across these entities, allowing them to jointly develop and refine models that improve network performance and service offerings without sharing sensitive or proprietary data.

[00133] Customization and Personalization: By leveraging data from various sources, VFL enables the development of customized services and personalized user experiences. This can lead to improved customer satisfaction and new revenue opportunities for operators. 3GPP SA2 Rel-19 Study in AIML (FS_AIML_CN)

[00134] A new Study Item (SID) on Core Network Enhanced Support for Artificial Intelligence (Al) / Machine Learning (ML) was approved in SP-231800 [1] in TSG SA Meeting #102 (Dec 2023). In WT#2 in the SID: in SP-231800 [1]: WT2: Study whether and what potential enhancements are needed to enable 5G system to assist in collaborative AI / ML operation involving 5GC / NWDAF and / or AF for “Vertical Federated Learning (VFL) ”. The work will be based only on and limited to the scope of justified use cases. NOTE 7: RAN and UE aspects are out of scope. Solutions based on interactions between the application client and 5GS are out of scope. The necessary communication between AF and UE application client to support the collaborative AI / ML operation is understood as no normative procedure impact. Horizontal FL procedure defined in R18 should be taken into account and reused whene ver possible. NOTE 8: coordination with SA6 is required.

[00135] The detailed description of Kl#2: 5GC Support for Vertical Federated Learning was documented in clause 5.2.2 of TR 23.700-84 [3], The issues to be addressed for Kl#2 by SA2 during Rel-19 study phase include: This key issue aims to provide solutions for enabling 5GC support for vertical federated learning (VFL) involving NWDAF and / or AF, where no raw data need to be exchanged but some level of coordination is still required when training and inference are performed on local models. In particular, datasets used for each local model need to share the same samples while holding different features. In Rel-18, ML model sharing between NWDAFs has been studied as a part of Horizontal Federated Learning. However, Federated learning between NWDAF andAF has not been studied (e.g. when the NWDAFs and / or AFs are in different domains, locations, regions etc). Vertical Federated Learning (VFL) can be considered as an alternative mechanism for distributed functionalities of an ML model. Note that, as scoped in Rel-19, NWDAF and / or AF may be involved for VFL. This Key Issue aims to study architecture enhancement to support VFL, which allows the cooperative AI / ML training and inference with the following aspects: Identify VFL use cases and under which conditions, andfor which entities these VFL use cases show that VFL is justified to train ML models. Whether and how to support architecture enhancement for supporting VFL for model training and / or inference. In particular: Whether and how the existing NF discovery and selection needs to be enhanced. Whether and how ML Model training and / or inference related procedures need to be enhanced to support VFL Whether and how to do performance monitoring for the ML model trained via VFL Whether and how to provide ML Models to the participants in the VFL training process. How to support sample and feature alignment among the participating network entities when performing VFL NOTE 1: Application layer-based VFL requiring communication between AFs and / or UEs application client, is out of scope. NOTE 2: During the study on this KI, consultation with SA3 is required for handling security aspects. NOTE 3: RAN and UE aspects are out of scope. NOTE 4: The existing procedures defined for Horizontal FL in 3GPP TS 23.288 [x] will be taken into account ■when studying the procedure for VFL.

[00136] In order to clarify the scenarios of using Vertical Federated Learning, VFL use cases are to be identified. One possible use case of implementing VFL is to deploy NWDAF to Support for Sample and Feature Alignment in VFL: It is well known in the AI / ML literature that VFL is a federated learning setting where multiple parties perform training on data sets that share the same sample space but differ in feature space. Because of this, an alignment in sample and feature spaces among participating entities is usually required before applying VFL. VFL further allows to perform joint training without exposing raw data or model parameters, the latter being a way in which VFL differs from HFL. TS 23.288 [2] provides NWDAF specification support for HFL but no VFL support is available. This use case proposes NWDAF support for VFL in analytics derivation by means of sample and feature alignment between the entities participating in VFL, where the main entity facilitating the VFL operation is NWDAF and other entities may be other NWDAF instances and / or AF(s). The motivation for this use case is mainly two-fold: i) in a multivendor scenario, VFL may be more suitable than HFL for multiple NWDAF deployments since such accuracy increase may be achieved without the need to share model parameters among the participating NWDAF from different vendors, and ii) VFL allows an enhanced accuracy of the NWDAF predictions as models trained via VFL usually generalize better by learning from a broader feature set. In PLMNs where multiple NWDAFs are deployed, each NWDAF instance may perform data collection locally according to their suitable data sources. Depending on the Analytics ID, the different NWDAF instances may share the sample ID space (e.g. S-NSSAI) or train on different sample ID spaces (e.g. UE IDs within their corresponding Area of Interest). Furthermore, the NWDAF instances are not all obliged to collect the same input data for the same Analytics ID as most input data is optional, thus their feature spaces may range from full to little overlap. Finally, while an AF may also participate on VFL supported by NWDAF, an alignment of samples would still be needed between the two entities, and feature alignment may also prove beneficial. Depending on the range of overlap in sample and feature spaces of the participating entities, VFL may be a more or less suitable technique to combine models at NWDAF. Hence, support for sample and feature alignment would allow the VFL supported by NWDAF to be more effective forthose scenarios that are suitable.

[00137] Based on the above background information and analysis of the given use case, compared to HFL, VFL is able to increase the accuracy of FL without sharing model parameters among the participating NWDAF from different vendors and also allows an enhanced accuracy of the NWDAF predictions as models.

[00138] As it has been agreed by SA2, the following issues should be addressed during SA2 Rel-19 study to support Vertical Federated Learning as documented in TR 23.700-84 [3]: This Key Issue aims to study architecture enhancement to support VFL, which allows the cooperative AI / ML training and inference with the following aspects: Identify VFL use cases and under which conditions, andfor which entities these VFL use cases show that VFL is justified to train ML models. Whether and how to support architecture enhancement for supporting VFL for model training and / or inference. In particular: Whether and how the existing NF discovery and selection needs to be enhanced. Whether and how ML Model training and / or inference related procedures need to be enhanced to support VFL Whether and how to do performance monitoring for the ML model trained via VFL Whether and how to provide ML Models to the participants in the VFL training process. How to support sample and feature alignment among the participating network entities when performing VFL

[00139] VFL is not able to increase the accuracy of FL without sharing model parameters among the participating NWDAF from different vendors, but also allows an enhanced accuracy of the NWDAF predictions as models. However, in the current 3GPP specifications, it is lack of solutions to address the above issues in the KI description on Kl#2: 5GC Support for Vertical Federated Learning.

[00140] In order to more fully support the Vertical Federated Learning by 5GC in different scenarios, new solutions are required to solve the above issues during SA2 Rel-19 study and normative phase. Vertical Federated Learning Enhancements Overview

[00141] Below is an overview of the steps / stages involved in the proposed implementation / approaches for enhancing vertical federated learning. This list of steps / stages is not exhaustive and only some of the steps / stages below may be used, and the steps / stages may be used in any combination.

[00142] 1. NWDAF registration: NWDAF containing Model Training Logical Function (MTLF) as VFL Server NWDAF and / or FL Client NWDAF registers to Network Repository Function (NRF) with its Network Function (NF) profile, which includes NWDAF NF, Analytics ID(s), Address information of NWDAF, Service Area, FL capability type information (i.e. FL server or FL client) and Time interval supporting FL as described in clause 5.2.

[00143] 2. Model Request: The model consumer (e.g. NWDAF containing Analytics Logical Function (AnLF) or NWDAF containing MTLF) sends a subscription / request to the VFL server NWDAF to retrieve an ML model, invoking the Nnwdaf_MLModelProvision_Subscribe I Nnwdaf_MLModellnfo_Request service (if consumer is NWDAF containing AnLF) or by invoking the Nnwdaf_MLModelTraining_Subscribe service I Nnwdaf_MLModelTraininglnfo_Request (if consumer is NWDAF containing MTLF) operation. This may include specifying the Analytics ID, desired ML model metrics (e.g., accuracy), accuracy reporting interval, and pre-determined status (accuracy threshold or time when the model is needed).P

[00144] 3. Participant Selection

[00145] 3.1 Once the VFL Server NWDAF is determined, the FL Server NWDAF discovers and selects other NWDAF(s) containing MTLF as VFL Client NWDAF(s) from NRF by invoking the Nnrf_NFDiscovery_Request service operation. NWDAF(s) containing MTLFs may also or alternatively be selected based on a predetermined list or other predetermined configuration as opposed to using a discovery process. For example, if a previous discovery process has been performed, the results may be reused.

[00146] 3.2 VFL Server NWDAF sends Federated Learning preparation request to the VFL Client NWDAF(s), using Nnwdaf_MLModelTraining_Subscribe or Nnwdaf_MLModelTraininglnfo_Request service with the ML Preparation Flag, to check if the VFL Client NWDAF(s) can meet the ML model training requirement (e.g. Analytics ID, ML Model Interoperability information, Available data requirement, Availability time requirement (time span needed for the FL process), etc.). In some examples, such a step may have been preformed at a previous time and / or the capabilities of VFL Client NWDAFs may have been pre-registered and thus known to the VFL Server NWDAF. In other examples, Federated Learning preparation requests may only be sent to VFL Client NWDAFs whose capabilities are known to be adequate given the model requirements.

[00147] 3.3 Data Alignment: Each VFL Client NWDAF aligns its dataset with the common samples identified across all tentative participating NWDAFs using the Nnwdaf_MLModelTraining_DataAlignment service.

[00148] 3.4 Each VFL Client NWDAF undertakes a detailed evaluation to ascertain its capability to meet the specified ML model training requirements. This multifaceted assessment includes one or more of 3.4.1 to 3.4.3 below. This capability evaluation may be performed in response to participant selection or may be performed at an earlier time and the result of the evaluation stored and / distributed to other entities such as VFL Server NWDAFs.

[00149] 3.4.1 Model Training Feasibility: Evaluating the technical feasibility to meet the ML model's training demands, considering the available computational resources and data processing capabilities.

[00150] 3.4.2 Model Access and Compatibility: Determining the ability to access or download the ML model, particularly when specific model details or parameters are shared within the request. This step helps to ensure that the FL Client NWDAF can effectively integrate and utilize the model within its existing infrastructure.

[00151] 3.4.3 Data Alignment Verification: Confirming the existence of common sample IDs with other NWDAFs, as established during the data alignment phase. This verification ensures that the FL Client NWDAF holds relevant and aligned data, making it a valuable participant in the federated learning endeavour.

[00152] 3.5 Based on this thorough evaluation, the VFL Client NWDAF makes an informed decision regarding its participation in the Federated Learning process. VFL Client NWDAF(s) invokes Nnwdaf_MLModelTraining_Notify or Nnwdaf_MLModelTraining_Subscribe response service operation or Nnwdaf_MLModelTraininglnfo_Request response service operation to indicate to the FL Server NWDAF whether it will join the FL procedure, the successful sample IDs identified (if any) and may include the reason in the response message if it cannot join the FL process. In some examples, capability information of the VFL Client NWDAF may already be known and thus such information can merely be compared to the criteria for determining whether to participate. In other examples, this assessment may be performed at the VFL Server NWDAF or other entity if the details of the VFL Client NWDAF are known to the VFL Server NWDAF or other entity.

[00153] 3.6 FL Server NWDAF determines the final list of VFL Client NWDAF(s) to be involved in the FL procedures based on the information received or otherwise determined in step 3.5

[00154] 4. Data Processing and Information Sharing: Each VFL Client NWDAF processes its dataset to generate predictive insights or intermediate data representations. These insights are then securely transmitted to the next designated VFL Client NWDAF(s), as orchestrated by the VFL Server NWDAF, using the Nnwdaf_MLModelTraining_DataProcessing_Sharing operation. This step ensures that the insights from one entity are integrated with or inform the processing by other entities, facilitating a collaborative approach to model refinement across the network. In the event of processing delays, the FL Client NWDAF may proactively notify the FL Server NWDAF to manage expectations and adjust timelines accordingly. In some examples, insights may be securely transmitted to one or more other designated VFL Client NWDAFs or one or more VFL Client NWDAFs may be skipped under control of the FL Server NWDAF.

[00155] 5. Insight Synthesis and Model Refinement: Beginning with the VFL Server NWDAF, each participating client synthesizes the received insights and applies them to refine their local model, using the Nnwdaf_MLModelTraining_lnsightSynthesis_Refinement operation. This involves analyzing the shared data representations or insights to improve the model's accuracy or performance, adjusting model parameters as necessary. Each NWDAF contributes to this iterative refinement process by passing enhanced insights or adjustments back to preceding participants, fostering a cumulative improvement in the model's efficacy. This collaborative refinement process ensures that the collective intelligence of all participants is leveraged, leading to a more robust and accurate federated model.

[00156] 6. Model Evaluation and Feedback: If the VFL procedure continues, VFL Server NWDAF sends Nnwdaf_MLModelTraining_Subscribe or Nnwdaf_MLModelTraininglnfo_Request notifying for a next round of Federated Training.

[00157] 7. End of Procedure: When the Federated Training procedure is complete, the VFL Server NWDAF requests the VFL client NWDAF(s) to terminate the FL procedure by invoking Nnwdaf_MLModelTraining_Unsubscribe service with a cause code that the FL process has finished. Then the VFL client NWDAF(s) terminates.

[00158] 8. After the training process is complete, the VFL Server NWDAF may send Nnwdaf_MLModelProvision_Notify that includes the combination of NWDAF clients involved in the process and needed for collaborative intelligence to the consumer. Procedure

[00159] A call flow diagram for the proposed procedure is shown in Figure 2. The steps of the procedure are not limited to those illustrated and described below and may include fewer or further steps. A subset including any combination of the steps described below may also be used.

[00160] The procedure below introduces support for VFL operation at NWDAF by means of enabling data alignment (i.e. sample and feature) among entities participating in the VFL training process with a new service. This new service allows guaranteeing that training samples are the same in the participating entities even though their training features are different. The solution requires an VFL server guiding the training process and VFL consumers following the VFL server instructions. NWDAF is the 5GC NF acting as VFL server.

[00161] The procedure in Figure 2 to support VFL operation at NWDAF is described step by step below.

[00162] 1a. VFL server (i.e. NWDAF) and VFL consumer (e.g. NWDAF) entities register to NRF. The registration may include one or more of their NF profiles, Analytics ID(s), Address information of NWDAF, Service Area, VFL capability type information (i.e. VFL server or VFL consumer) and Time interval supporting VFL. The latter parameter can be the same as Time interval supporting FL described in clause 5.2 of TS 23.288 [2],

[00163] 1b. The NWDAF instance with the capability of acting as VFL server is discovered upon a request by the consumer NF (i.e. NF that subscribes to / requires the analytics) or by an NWDAF containing AnLF.

[00164] 2a. [CONDITIONAL] If the VFL server does not include inference capabilities (i.e. NWDAF does not contain AnLF), the consumer NF sends an analytics subscription or request to an NWDAF containing AnLF supporting the requested Analytics ID.

[00165] 2b. [CONDITIONAL] If step 2a is executed, the NWDAF containing AnLF sends a subscription or request to the VFL server NWDAF to retrieve an AI / ML model, invoking the Nnwdaf_MLModelProvision_Subscribe or Nnwdaf_MLModellnfo_Request service. This includes specifying the Analytics ID, desired ML model metrics (e.g., accuracy), accuracy reporting interval, and pre-determined status (accuracy threshold or time when the model is needed).

[00166] 3. [CONDITIONAL] If the VFL server NWDAF contains AnLF, the consumer NF may directly subscribe or request to analytics from the VFL server NWDAF.

[00167] 4. The VFL server NWDAF discovers and selects other VFL consumers (e.g. NWDAF containing MTLF) from NRF by invoking the Nnrf_NFDiscovery_Request service operation. In some examples, VFL consumers may be considered to subscribe to model training services from a VFL server NWDAF.

[00168] 5. The VFL server NWDAF sends a VFL preparation request to the VFL consumers, using Nnwdaf_MLModelTraininglnfo_Request (or Nnwdaf_MLModelTraining_Subscribe) service operation with the ML Preparation Flag, to check if the VFL consumer(s) can meet the ML model training requirement (e.g. Analytics ID, ML Model Interoperability information, Available data requirement, Availability time requirement, etc.). The VFL consumer(s) may respond to the VFL server NWDAF indicating whether they will join the VFL operation and may include the reason in the response message if it cannot join the VFL operation.

[00169] NOTE: The selection of VFL consumer(s) by the VFL server NWDAF may happen after step 5, fully or partially. The selection of VFL consumer(s) by the VFL server NWDAF may also be performed or refined after step 8.

[00170] 6. The VFL server NWDAF sends a request for data alignment (i.e. samples and features) to the VFL consumer(s). This new service / service operation is required to facilitate the alignment of datasets from different sources in a VFL environment. It ensures that participating entities work with a common set of samples without revealing sensitive data. In addition, these samples are the intersection of different datasets where each entity has different features (information or attributes) for the same set of entities or individuals.

[00171] Required inputs of this service operation include dataset identifiers (i.e. unique identifiers for the datasets held by each VFL participating entity), alignment technique (i.e. specific methods or algorithms to be used for data alignment, such as Private Set Intersection (PSI), feature hashing, or other techniques that ensure data privacy and integrity) and Notification Target Address. Optional inputs may include an alignment correlation ID, expiry time, and additional data alignment information such as challenges or discrepancies encountered in the data alignment process.

[00172] 7. Each VFL consumer performs data alignment pre-processing. This is required for each VFL to ascertain its capability to meet the AI / ML model training requirements. Data alignment pre-processing may include model training demands feasibility assessment, model access and compatibility, data alignment verification, etc.

[00173] 8. The VFL consumer(s) make(s) a decision regarding its participation in VFL and notifies the VFL server NWDAF whether it will join the VFL operation along the successful sample IDs identified (if any). The VFL consumer(s) may also include the reason in the response message if it cannot join the VFL operation. The VFL server NWDAF may then select / finalise the VFL consumers to involve in the VFL.

[00174] 9. In the first training iteration, the VFL server NWDAF triggers the VFL training process by invoking the service operation Nnwdaf_MLModelTraining_Subscribe. Subsequent iterations of the VFL training process where intermediate training results are shared are also coordinated by the VFL server NWDAF via the same service operation, facilitating a collaborative approach to model refinement across the participating entities.

[00175] 10. VFL consumers may interact with each other during the VFL training process.

[00176] 11. VFL consumers notify of training results to the VFL server NWDAF via Nnwdaf_MLModelTraining_Notify service operation.

[00177] 12. Once training is complete, participating entities in the VFL process collaborate with each other to perform joint inference.

[00178] 13. [CONDITIONAL] If steps 2a and 2b were executed, NWDAF containing AnLF provides analytics to the NF consumer.

[00179] 14. [CONDITIONAL] If step 3 was executed, VFL server NWDAF provides analytics to the NF consumer.

[00180] Regarding impacts on services, entities and interfaces, NWDAF is required to support the new data alignment service and the existing service Nnwdaf_MLModelTraining needs enhancements to support sharing of intermediate training results for VFL.

[00181] In another embodiment, the following procedure, with reference to Figure 3, can be followed to support VFL with NWDAF and AF as Participants.

[00182] NOTE 1: In this solution, the VFL server coordinates the VFL operation and acts as active participant with access to labels.

[00183] NOTE 2: Participants in this solution can be either active participants with access to labels or passive participants without access to labels.

[00184] NOTE 3: VFL Participants may also be called VFL Clients.

[00185] The procedure in Figure 3 to support VFL operation at NWDAF is described step by step below.

[00186] 1. VFL server (i.e. NWDAF) and VFL participant entities (NWDAF, AF) entities register to NRF. The registration may include their NF profiles, Analytics ID(s), Address information of NWDAF, Service Area, VFL capability type information (i.e. VFL server or VFL participant type) and Time interval supporting VFL. The latter parameter can be the same as Time interval supporting FL described in clause 5.2 of TS 23.288 [5],

[00187] NOTE 3: VFL participant type parameter can have the value of ‘active’ or ‘passive’.

[00188] 2. The VFL server and participants are discovered via NRF by invoking the Nnrf_NFDiscovery_Request service operation.

[00189] NOTE 4: Details of the discovery mechanism are not within the scope of this solution.

[00190] 3. The VFL server sends a VFL preparation request to the VFL consumers. For NWDAF participants, the existing service operations Nnwdaf_MLModelTraininglnfo_Request or Nnwdaf_MLModelTraining_Subscribe may be reused and enhanced, or a new service may be defined. For AF participants, a new AF / NEF service is required. Either way, the ML Preparation Flag is provided to check if the VFL participants can meet the ML model training requirement (e.g. Analytics ID, ML Model Interoperability information, Available data requirement, Availability time requirement, etc.). The VFL participants may respond to the VFL server indicating whether they will join the VFL operation and may include the reason in the response message if it cannot join the VFL operation.

[00191] 4. The selection of VFL participants by the VFL server may happen here, fully or partially. The selection of VFL participants by the VFL server may also be performed or refined in step 8.

[00192] 5. The VFL server sends a request for data alignment (i.e. samples and features) to the VFL participants, via NEF for AF participants. This new service / service operation is required to facilitate the alignment of datasets from different sources in a VFL environment. It ensures that participating entities work with a common set of samples without revealing sensitive data. In addition, these samples are the intersection of different datasets where each entity has different features (information or attributes) for the same set of entities or individuals.

[00193] Required inputs of this service operation include dataset identifiers (i.e. unique identifiers for the datasets held by each VFL participant), alignment technique (i.e. specific methods or algorithms to be used for data alignment, such as Private Set Intersection (PSI), feature hashing, or other techniques that ensure data privacy and integrity) and Notification Target Address. Optional inputs may include an alignment correlation ID, expiry time, and additional data alignment information such as challenges or discrepancies encountered in the data alignment process.

[00194] 6. Each VFL participant performs data alignment pre-processing. This is required for each VFL participant to ascertain its capability to meet the AI / ML model training requirements. Data alignment pre-processing may include model training demands feasibility assessment, model access and compatibility, data alignment verification, etc.

[00195] 7. The VFL participants notify the VFL server the result of the data alignment, via NEF for AF participants. Furthermore, the VFL participants may provide the VFL server with a decision regarding its participation in the VFL operation along the successful sample IDs identified (if any). The VFL participants may also include the reason in the response message if it cannot join the VFL operation.

[00196] 8. If not completed at step 4, the selection of VFL participants by the VFL server may be performed or refined based on the inputs received by the VFL server from the VFL participants.

[00197] 9. The VFL training process starts on this step, where intermediate training results are shared and coordinated by the VFL server, facilitating a collaborative approach to model refinement across the VFL participating entities. In the first iteration, the VFL server triggers the VFL training process by invoking a ML Model Training service subscription operation from the VFL Participant #1 (e.g. NWDAF). In the subsequent iteration, the VFL server may need to use the ML Model Training service notification operation with the Participant #2 (or last participant if more than two) (e.g. AF via NEF) to enable model refinements in each participant. Subsequent iterations may require the VFL server to subscribe or notify from / to either participant using the ML Model Training service. While the existing Nnwdaf_MLModelTraining service can be used for NWDAF participants, a new service at the NEF / AF is required to support this functionality. In the last iteration, the VFL server informs the VFL participants that the VFL training process is completed via a suitable flag, steps 10 through 12 are skipped, and the VFL training loop is terminated.

[00198] 10a.The VFL Participant#! may perform computation to locally train its model. The computation may lead either to intermediate training results to be shared to the next participant in step 11, or to refine a previously trained model and notify the VFL server in step 13.

[00199] 10b.The VFL Participant #1 may share its intermediate training results via NEF with the AF participant, using the same service operations as in step 9.

[00200] 10c.Participant #2 (i.e. last VFL participant) performs computation to locally train its model. The computation may lead either to intermediate training results to be shared with the VFL server in step 11, or to refine a previously trained model and the previous VFL participant in step 10d.

[00201] 10d.The VFL Participant#! performs computation to refine its local model.

[00202] !!. Intermediate training results and / or model refinement results are shared with the VFL server, via NEF if from AF, using the same ML model training service used in step 9.

[00203] 12. The VFL server performs further VFL computation.

[00204] 13. A consumer NF subscribes to or requests analytics from the NWDAF hosting VFL server functionality.

[00205] 14. A distributed VFL inference process is triggered by the VFL server invoking a ML model inference request from Participant #1. This step can be executed with a new service or by enhancing the existing ML Model Training service since the required VFL computation is essentially the same for inference and some training cycles.

[00206] 15a. Participant #1 performs local inference computation.

[00207] 15b. Participant#! shares intermediate inference results with Participant #2 via NEF when Participant #2 is an AF. In that case, the NEF / AF service used may be new or an enhanced version of the new NEF / AF service already used in step 9.

[00208] 15c.Participant #2 performs local inference computation.

[00209] 16. Participant #2 (i.e. the last participant) notifies the result of the distributed inference process to the VFL server using the same service as in step 14 via the notification operation.

[00210] 17. The VFL server performs further inference computation and derives the requested analytics.

[00211] 18. The derived analytics are delivered to the NF consumer. Services - Nnwdaf_MLModelTraining Service

[00212] Explained below are service operations that may be used to request or implement any of the steps described above with respect to the overview and the procedure.

[00213] The service operations described below can also be standalone services or service operations part of a different service (e.g. Nnwdaf _VFL service, Nnwdaf _DataAlignment service, etc.). Furthermore, although information is described as required and / or optional, embodiments are not limited to including all the required information. For example, with respect to the Nnwdaf_MLModelTraining_DataAlignment Service operation (and the other service operations), the message may be mandated to include only some of the Inputs / Outputs, Required.

[00214] Nnwdaf MLModelTraininq DataAliqnment Service Operation Service Operation Name: Nnwdaf_CollaborativeTraining_DataAlignment Description: This operation facilitates the alignment of datasets from different sources in a vertical FL environment. It ensures that participating entities work with a common set of samples without revealing sensitive data. In the context of VFL, "common samples" refer to the data points or records that are shared across the datasets of different participating entities, based on a common identifier. These samples are the intersection of different datasets where each entity has different features (information or attributes) for the same set of entities or individuals. The service employs techniques such as, but not limited to, Private Set Intersection (PSI), Private Set Union (PSU) or Federated Learning without Revealing InterSecTions (FLORIST), etc. to securely identify overlapping data points across datasets. When a request for data alignment is accepted by the NWDAF containing MTLF, the consumer NF (e.g., another NWDAF instance) receives a unique identifier (Alignment Correlation ID) for managing this alignment process. The NWDAF may modify the data alignment parameters based on operator policy and configuration. Inputs, Required: • Alignment Request: A request from the VFL Server NWDAF to the VFL Client NWDAFs, instructing them to align their datasets. This request includes guidelines or parameters for alignment, such as identifying common entities or samples. • Dataset Identifiers: Unique identifiers for the datasets held by each VFL Client NWDAF, to facilitate the alignment process. - Alignment Technique Specification: specific methods or algorithms to be used for data alignment, such as Private Set Intersection (PSI), feature hashing, or other techniques that ensure data privacy and integrity. Notification Target Address: Address for sending notifications related to the data alignment process. Inputs, Optional: • Alignment Correlation ID: Used for modifying an existing data alignment subscription. • Data Alignment Information: Detailed information about the alignment process, including any challenges or discrepancies encountered and how they were resolved. This includes a summary of the aligned dataset, such as the number of common samples identified. • Expiry Time: Time after which the data alignment request expires. • Data Privacy Settings: Specifications for handling sensitive data during alignment. Outputs Required: • When the subscription is accepted: Alignment Correlation ID (for managing the alignment process), Expiry Time (if applicable based on operator policy). Outputs, Optional: None.

[00215] Nnwdaf MLModelTraining DataProcessing Sharing Service Operation Service Operation Name: Nnwdaf_MLModelTraining_DataProcessing_Sharing .

[00216] Description: This operation facilitates the collaborative integration and sharing phase within the VFL framework. It enables each participating NWDAF to process its dataset, integrate insights, and share the resulting data representations with subsequent participants in the VFL ecosystem. This mechanism is pivotal for combining diverse data insights across the network, enhancing the federated model's predictive capabilities while upholding the stringent privacy requirements of each participant's data. Inputs, Required: • Local Model Insights: The insights or data representations derived from processing the local dataset with the current model parameters. These insights are crucial for enriching the federated model's learning context. • Local Data Features: Specific features from the local dataset that have been processed to generate the aforementioned insights. This includes any transformations or feature engineering steps applied to the data. Inputs, Optional: • Privacy Enhancement Techniques: Parameters or methods employed to safeguard the privacy of shared data insights, such as homomorphic encryption, secure multi-party computation, or differential privacy mechanisms. • Preceding Insights: Insights or data representations received from previous participants in the VFL chain. These are optionally incorporated into the local dataset to enrich the model's learning context. Outputs, Required: • Integrated Data Representations: The enhanced data representations ready to be shared with the next participant. These representations are a synthesis of local insights and, optionally, insights received from preceding models, tailored for the next phase of collaborative learning. Outputs, Optional: • Integration Metadata: Supplementary information regarding the data integration process, including computational metrics, resource utilization, and any challenges or anomalies encountered. This metadata can provide valuable feedback for optimizing the VFL process.

[00217] Nnwdaf MLModelTraining Backward Propagation Service Operation Service Operation Name: Nnwdaf_MLModelTraining _BackwardPropagation Description: This operation orchestrates the model optimization phase within the VFL framework, focusing on the refinement of local models through collaborative insights. It encompasses the iterative adjustment of model parameters by leveraging gradient, or other learning information, derived from the collective learning process. This phase is critical for enhancing the model's accuracy and performance by incorporating feedback from across the VFL network, ensuring that each NWDAF's model evolves in alignment with shared objectives while maintaining data privacy. Inputs, Required: • Received Insights: Insights or gradient information received from subsequent participants in the VFL chain, or the aggregate loss insights in the case of the concluding participant. These insights are pivotal for recalibrating the local model's parameters. • Local Processing Insights: Insights derived from the local model's processing, utilized to calculate adjustments for the model's parameters. Inputs, Optional: • Privacy Enhancement Mechanisms: Techniques or parameters implemented to safeguard the privacy of shared insights during the model optimization phase, such as secure multi-party computation or noise addition techniques. Outputs, Required: • Model Parameter Adjustments: Adjustments calculated for the local model's parameters, aimed at optimizing the model's performance based on the collaborative insights. These adjustments are foundational for the iterative enhancement of the model and may be shared with preceding participants to foster collective model refinement. Outputs, Optional: • Optimization Metadata: Supplementary information regarding the optimization process, including details on computational efficiency, resource allocation, and any obstacles or anomalies identified. This metadata is instrumental in diagnosing the optimization process, facilitating continuous improvement of the VFL methodology.

[00218] It will be appreciated that examples of the present disclosure may be realized in the form of hardware, software or a combination of hardware and software. Certain examples of the present disclosure may provide a computer program comprising instructions or code which, when executed, implement a method, system and / or apparatus in accordance with any aspect, example and / or embodiment disclosed herein. Certain embodiments of the present disclosure provide a machine-readable storage storing such a program.

[00219] The Annex to this description (found below) discloses one or more further techniques according to the present disclosure. The skilled person will appreciate that the techniques disclosed in the Annex may be used together with the techniques disclosed herein in any suitable combination.

[00220] Figure 4 is a block diagram of an exemplary network entity / funotion that may be used in examples of the present disclosure, such as the techniques disclosed in relation to any of the preceding figures. For example, any of the network entities, network function etc. may be provided in the form of the network entity illustrated in Figure 4. The skilled person will appreciate that a network entity / function may be implemented, for example, as a network element on a dedicated hardware, as a software instance running on a dedicated hardware, and / or as a virtualised function instantiated on an appropriate platform, e.g. on a cloud infrastructure.

[00221] The entity 400 comprises a processor (or controller) 401, a transmitter 403 and a receiver 405. The receiver 405 is configured for receiving one or more messages from one or more other network entities, for example as described above. The transmitter 403 is configured for transmitting one or more messages to one or more other network entities, for example as described above. The processor 401 is configured for performing one or more operations, for example according to the operations as described above.

[00222] Figure 5 illustrates a method according to an example of the present disclosure. The method is performed by a server entity, e.g. a VFL server entity.

[00223] In operation 510, the server entity, based on a message received from a service consumer, initiates the VFL procedure.

[00224] In operation 520, the server entity transmits a request for VFL inference to at least one VFL client entity.

[00225] In operation 530, the server entity receives intermediate inference results generated by the at least one VFL client entity.

[00226] In operation 540, the server entity performs inference computation on the received intermediate inference results.

[00227] Figure 6 illustrates a method according to an example of the present disclosure. The method is performed by a VFL client entity.

[00228] In operation 610, the VFL client entity receives a request for VFL inference from a server entity.

[00229] In operation 620, the VFL client entity, based on the request, obtains intermediate inference results using a local ML model.

[00230] In operation 630, the VFL client entity transmits the intermediate inference results to the server entity.

[00231] Further examples in accordance with the present disclosure are set out below, where the examples may be combined in any appropriate form and also combined with any of the approaches set out above.

[00232] For all of the examples / aspects / embodiments etc. described above / herein, it should be considered that the corresponding features / operations apply in any order or combination, and that furthermore there exists the possibility to omit one or more features / operations.

[00233] Moreover, for all of the examples, embodiments, aspects etc. above, these apply to at least LTE, NR, NR NTN or loT NTN (note this list is merely to give some examples and should not be seen as limiting), including any related signalling / messages on any of the inferences X2, Xn, NG, S1, F1, etc (again, this list is merely to give some examples and should not be seen as limiting).It will be appreciated that, in each example / embodiment / aspect etc. described above, one or more features or operations may be omitted, modified or moved (e.g., to change the order of the features or the operations), if desired and appropriate.

[00234] Additionally, where the figures illustrating example method flows include text in relation to a specific step / operation, it will be appreciated that this text is simply an example of the corresponding step / operation, where a more general definition (such as may be found in the description of the corresponding step) may apply for the step / operation.

[00235] Additionally, regarding all of the above, one or more features or operations etc. from any example / embodiment may be combined with features or operations from any other example / embodiment. That is, the present disclosure should be considered to include all combinations of examples / embodiments disclosed herein, as appropriate, as well as combinations of individual features within and between each example / embodiment, as appropriate.

[00236] The techniques described herein may be implemented using any suitably configured apparatus and / or system. Such an apparatus and / or system may be configured to perform a method according to any aspect, embodiment or example disclosed herein. Such an apparatus may comprise one or more elements, for example one or more of receivers, transmitters, transceivers, processors, controllers, modules, units, and the like, each element configured to perform one or more corresponding processes, operations and / or method steps for implementing the techniques described herein. For example, an operation / function of X may be performed by a module configured to perform X (or an X-module). The one or more elements may be implemented in the form of hardware, software, or any combination of hardware and software.

[00237] It will be appreciated that examples of the present disclosure may be implemented in the form of hardware, software or any combination of hardware and software. Any such software may be stored in the form of volatile or non-volatile storage, for example a storage device like a ROM, whether erasable or rewritable or not, or in the form of memory such as, for example, RAM, memory chips, device or integrated circuits or on an optically or magnetically readable medium such as, for example, a CD, DVD, magnetic disk or magnetic tape or the like.

[00238] It will be appreciated that the storage devices and storage media are embodiments of machine-readable storage that are suitable for storing a program or programs comprising instructions that, when executed, implement certain examples of the present disclosure. Accordingly, certain examples provide a program comprising code for implementing a method, apparatus or system according to any example, embodiment and / or aspect disclosed herein, and / or a machine-readable storage storing such a program. Still further, such programs may be conveyed electronically via any medium, for example a communication signal carried over a wired or wireless connection.

[00239] While the disclosure has been shown and described with reference to certain examples, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the disclosure.

[00240] The reader's attention is directed to all papers and documents which are filed concurrently with or previous to this specification in connection with this application and which are open to public inspection with this specification, and the contents of all such papers and documents are incorporated herein by reference.

Claims

1. A server entity for performing a vertical federated learning (VFL) procedure, the server entity configured to:based on a message received from a service consumer, initiate the VFL procedure;transmit a request for VFL inference to at least one VFL client entity;receive intermediate inference results generated by the at least one VFL client entity; and perform inference computation on the received intermediate inference results.

2. The server entity of claim 1, further configured to discover or select the at least one VFL client entity to participate in the VFL procedure.

3. The server entity of any previous claim, further configured to:derive analytics according to the message, based on the inference computation; and transmit the derived analytics to the service consumer.

4. The server entity of any one of the previous claims, wherein the message is an analytics request received from the service consumer.

5. The server entity of any one of the previous claims, further configured to:register, to network repository function (NRF), information including at least one of network function (NF) profile, analytics ID(s), service area, VFL capability information, and time interval supporting VFL.

6. The server entity of any one of the previous claims, further configured to:transmit a VFL preparation request to one or more VFL client, the VFL preparation request including at least one of analytics ID, ML model interoperability information, available data requirement, availability time requirement, or information for checking whether a ML model training requirement can be met.

7. The server entity of claim 6, further configured to transmit dataset identifiers to the one or more VFL client entity.

8. The server entity of claim 6 or claim 7, further configured to:receive, from each of the one or more VFL client entity, information including an indication of whether said VFL client entity will participate; andselect the at least one VFL client entity from among the one or more VFL client entity based on the received information.

9. The server entity of claim 8, wherein the received information further comprises a reason why said VFL client entity cannot participate.

10. The server entity of any one of the previous claims, further configured to: transmit, to the at least one VFL client entity, a request to perform ML model training; and receive, from the at least one VFL client entity, intermediate training results.

11. The server entity of claim 10, further configured to perform VFL computation based on the received intermediate training results.

12. The server entity of claim 11, wherein the intermediate training results relate to a ML model associated with the VFL inference.

13. A vertical federated learning (VFL) client entity for participating in a VFL procedure, the VFL client entity configured to:receive a request for VFL inference from a server entity;based on the request, obtain intermediate inference results using a local machine learning (ML) model; andtransmit the intermediate inference results to the server entity.

14. The VFL client entity of claim 13, further configured to share the intermediate inference results with another VFL client entity.

15. The VFL client entity of claim 13 or claim 14, further configured to register, to network repository function (NRF), information including at least one of network function (NF) profile, analytics I D(s), service area, VFL capability information, and time interval supporting VFL.

16. The VFL client of any one of claims 13 to 15, further configured to receive a VFL preparation request from the server entity, the VFL preparation request including at least one of analytics ID, ML model interoperability information, available data requirement, availability time requirement, or information for checking whether a ML model training requirement can be met.

17. The VFL client entity of claim 16, further configured to:based on the VFL preparation request, determine capability to meet model training requirements; andtransmit, to the server entity, information including an indication of whether the VFL client entity will participate in the VFL procedure.

18. The VFL client entity of any one of claims 16 to 17, further configured to receive dataset identifier from the server entity.

19. The VFL client entity of any one of claims 16 to 18, wherein the transmitted information further includes a reason why the VFL client entity cannot participate.

20. The VFL client entity of any one of claims 13 to 19, further configured to:receive, from the server entity, a request to perform ML model training; andtrain the local ML model;wherein the trained local ML model is used to obtain the intermediate inference results.

21. The VFL client entity of claim 20, further configured to:obtain intermediate training results of the trained local ML model, and share the intermediate training results with the server entity; orrefine a previously trained model to obtain the trained local ML model and share the model refinement results with the server entity.

22. The VFL client entity of claim 19, wherein the intermediate training results or the model refinement results are shared using a same ML model training service as used for receiving the request to perform ML model training.

23. The VFL client entity of any one of claims 20 to 22, further configured to share the intermediate training results with another VFL client entity.

24. A method of a server entity for performing a vertical federated learning (VFL) procedure, the method comprising:based on a message received from a service consumer, initiating the VFL procedure;transmitting a request for VFL inference to at least one VFL client entity;receiving intermediate inference results generated by the at least one VFL client entity; andperforming inference computation on the received intermediate inference results.

25. A method of a vertical federated learning (VFL) client entity for participating in a VFL procedure, the method comprising:receiving a request for VFL inference from a server entity;based on the request, obtaining intermediate inference results using a local machine learning (ML) model; andtransmitting the intermediate inference results to the server entity.

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