Abstract: Disclosed are various example embodiments which may be configured to: receive, from a distributed node, local dataset information comprising characteristics of a local dataset of the distributed node, assign a score to the distributed node and/or determine whether the distributed node is a potential malicious distributed node based on the local dataset information, determine whether to select the distributed node for training a local model for managing a network in a federated learning mechanism based on the score assigned to the distributed node and/or whether the distributed node is a potential malicious distributed node, and send, to the distributed node, an indication as to whether the distributed node has been selected for training a model for managing a network in a federated learning mechanism.

Abstract: There are provided measures for machine learning model renewal. Such measures exemplarily comprise receiving a first machine learning model message including a first machine learning inference model, obtaining network related input data, feeding said first machine learning inference model with said network related input data, receiving, upon unsuitability of said first machine learning inference model for an experienced network condition, a second machine learning model message including a second machine learning inference model, and replacing said first machine learning inference model with said second machine learning inference model.

Abstract: There is provided an apparatus comprising: at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to receive, at an analytics producer, a request for analytics, from an analytics consumer, wherein the request comprises a first time period, TA, determine a second time period, DS for an expected time to provide the requested analytics at the analytics producer, provide the analytics based on the request in a third time period, TD, determine, based on TA, DS and TD, which of at least one of a plurality of parameters is met and increment a counter for the determined parameter.

Abstract: An apparatus for use by a communication network element or function configured to act as a management controller in a communication network, the apparatus comprising at least one processing circuitry, and at least one memory for storing instructions to be executed by the processing circuitry, wherein the at least one memory and the instructions are configured to, with the at least one processing circuitry, cause the apparatus at least: to conduct a creation and activation of at least one data source function instance for configuring a data source being not standardized for usage in the communication network to provide data to a data consumer formed by a communication network element or function in a data format allowing the data consumer to process the data, wherein the creation and activation comprises associating the data source function instance to meta data describing either a non-communication network standardized data type or a proprietary data type using attributes defined to the respective data type,

Abstract: An apparatus configured to: transmit, to an analytics producing entity, an indication to perform action impact analysis for a machine learning model; receive, from the analytics producing entity, a message comprising at least one of: analytics, a prediction, or a recommendation from the machine learning model; select at least one action based, at least partially, on the received message; enforce the at least one selected action; obtain an action impact analysis for the at least one enforced action and the machine learning model; evaluate the action impact analysis; and determine whether to request a change regarding the machine learning model in response to the evaluation of the action impact analysis.

Abstract: An apparatus for use by a communication network element or communication network function configured to operate as an analytics entity and having an analytics logical function. The apparatus is caused to: receive a request, from a service consumer, for provision of a custom analytics service, process data retrieved from the request for determining whether a model for providing the requested custom analytics service is prepared, and in case the determination is negative, determine and select a model training entity having a model training logical function which is able to create a model for custom analytics and has access to data required for the requested custom analytics service, request, from the selected model training entity, to obtain a trained model capable of providing the requested custom analytics service and forward the information specifying the custom analytics service to the selected model training entity.

Abstract: Techniques for detection of abnormal network function service usage in a communication network are disclosed. For example, a method comprises obtaining, at a first network entity, one or more service requests previously received by a second network entity for a service which the second network entity is configured to provide in a communication network. The method further comprises obtaining, at the first network entity, an analysis of the one or more service requests previously received by the second network entity for the service. The method further comprises obtaining, at the first network entity, an expected service usage for the service from the analysis of the one or more service requests. The method may then compare incoming service requests to the expected service usage to detect a given condition, e.g., an abnormal condition, so that at least one action can be taken.

Abstract: There are provided measures for enabling/realizing inference-aware ML (machine learning) model provisioning, e.g. to support network data analytics, in a mobile/wireless communication system. Such measures exemplarily comprise that ML model request information, including model-related information indicating one or more properties of a requested ML model and inference-related information indicating one or more properties of execution of inference based on the requested ML model, is provided from a first network entity (representing a service consumer of a network data analytics service) to a second network entity (representing a service provider of the network data analytics service), the second network entity specifies an ML model to be provisioned based on the ML model request information, and ML model information about the specified ML model is provided from the second network entity to the first network entity.

Abstract: Systems, methods, apparatuses, and computer program products that enable representative user equipment (UE) sampling for UE-related analytics services are provided.