Abstract: Embodiments herein relate to a method performed by a network node (10) for handling monitoring of applications and/or services in a communication network. The network node (10) obtains an indication associated with a monitoring session to monitor a metric of a service and/or application; and also obtains a location of deployment of the service and/or application. The network node identifies one or more ongoing monitoring sessions for monitoring one or more metrics based on the metric associated with the indication and the location of deployment of the application or service. The network node (10) then selects a monitoring process based on the identification; and triggers the selected monitoring process.

Abstract: A computer-implemented method is provided for determining whether a machine learning model in a candidate source domain is suitable for use in a target domain, wherein the machine learning model is trained with one or more features. The method comprises for each feature: determining one or more target measurement configurations indicating how data for the feature can be generated in the target domain; for each feature, performing, for each of a plurality of candidate source domains, the steps of: determining one or more source measurement configurations indicating how data for the feature can be generated in the candidate source domain, determining a similarity metric indicative of a similarity between the one or more source measurement configurations and the one or more target measurement configurations; and based on the similarity metrics determined for each feature for the plurality of candidate source domains, selecting one or more selected source domains from the plurality of candidate source domains.

Abstract: A method by a client computing device having one or more sensors for collecting data, includes obtaining information identifying a first set of measurable features, each feature being associated with a measurement specification. For each feature of the first set of measurable features, determining whether there is at least one sensor of the one or more sensors satisfying the associated measurement specification. If there is at least one sensor of the one or more sensors satisfying the associated measurement specification, estimating a resource usage. Determining a first subset of the first set of measurable features and sending information identifying the first subset of the first set of measurable features. When the client computing device is determined to belong to the first group of computing devices, performing training of the machine learning model using the first group of computing devices.

Abstract: A method is provided. The method includes generating a first data by using a first decoder model with a first set of target features, wherein the first decoder model is based on the first source domain. The method includes updating a final set of target features and final data based on the generated first data. The method includes generating a second data by using a second decoder model with a second set of target features, wherein the second data that is generated is conditioned on the first set of target features and wherein the second decoder model is based on the second source domain. The method includes updating the final set of target features and final data based on the generated second data. The method includes training a target-domain model using the final data and the final set of target features.

Abstract: There is provided a method comprising: acquiring (110) data associated with the routes of mobile communication devices; determining (120) a subset of mobile communication devices which share a same route for a given amount of time; determining (130) base stations located along the shared route; estimating (140) points of time at which the subset of mobile communication devices are in coverage areas of respective base stations; determining (150) an amount of required processing resources at the base stations and/or at the subset of mobile communication devices; and generating (160) a schedule for a plurality of federated learning tasks to be performed, based on priority levels associated with the federated learning tasks, estimated points of time at which the subset of mobile communication devices are in coverage areas of the base stations, and the amount of required processing resources at the base stations and/or at the subset of mobile communication devices.

Abstract: A method for machine-learning adaptation comprises identifying (110) a plurality of machine-learning source domain candidates and calculating (120), for each of the identified machine-learning source domain candidates, a diversity metric, where the diversity metric represents a marginalized measure of sample diversity of the respective machine-learning source domain candidate. The method further comprises selecting (130) the identified machine-learning source domain candidates having a highest diversity metric among the calculated diversity metrics and applying (140) the selected machine-learning source domain candidate to a target domain in a new or changed execution environment. The diversity metric may be calculated based on information theoretic measures, for example, such as based on a one-parameter measure of generalized entropy.

Abstract: There is provided a method comprising: acquiring (110) sensor data related to an object; using the first learning module, identifying (120) the object based on the acquired sensor data using a first learning module and determining (130) a user associated with the identified object; determining (140) a timestamped location of the object based on at least one of the acquired sensor data and one or more locations of the one or more sensors; performing (150) a first analysis to determine whether the current status of the object contains an anomaly based on one or more predefined rules stored in a knowledge base; performing (160) a second analysis to determine whether the current status of the object contains an anomaly, using a second learning module; and validating (170) whether the current status of the object contains an anomaly based on results of the first analysis and results of the second analysis.

Abstract: A method in a first node of a communications network for training a machine learning model comprises receiving a first message comprising instructions for training the machine learning model using a distributed learning process. The method then comprises responsive to receiving the first message, acting as an aggregator in the distributed learning process for a subset of other nodes selected by the first node from a plurality of nodes that have an established radio channel allocation with the first node, by causing the subset of other nodes to perform training on local copies of the machine learning model and aggregating the results of the training by the subset of other nodes.

Abstract: The present disclosure relates to methods and devices (101, 106) of controlling reactive power of a power grid. In an aspect, a method of a radio base station (101) of controlling reactive power of a power grid is provided. The method comprises measuring (S101) an electrical property indicating a level of the reactive power supplied by the power grid to which the radio base station (101) is connected, and performing (S102) an action to stabilize the level of the reactive power of the power grid upon the measured electrical property reaching a certain value.

Abstract: Methods, systems, and apparatuses are presented for grouping worker nodes in a machine learning system comprising a master node and a plurality of worker nodes, the method comprising grouping each worker node of the plurality of worker nodes into a group of a plurality of groups based on characteristics of a data distribution of each of the plurality of worker nodes, subgrouping worker nodes within the group of the plurality of groups into subgroups based on characteristics of a worker neural network model of each worker node from the group of the plurality of groups, averaging the worker neural network models of worker nodes within a subgroup to generate a subgroup average model, and distributing the subgroup average model.

Abstract: Methods, systems, apparatuses and computer programs are presented for developing machine-learning models. A method for decentralized machine learning in a target worker node comprises: receiving a plurality of adapted neural network models from a plurality of worker nodes, wherein each of the adapted neural network models is generated by training a worker node neural network using local data of the worker node from among the plurality of worker nodes; selecting, from the plurality of adapted neural network models, a set of adapted neural network models that satisfy performance criteria when local data of the target worker node is input; and averaging the set of adapted neural network models to generate an average model.

Abstract: A method by a computing device for dynamically configuring a network comprising a plurality of computing devices configured to perform training of a machine learning model is provided. The method includes dynamically identifying a change in a state of a leader computing device, wherein the leader computing device includes one of a server computing device and a client computing device and wherein the plurality of computing devices include server computing devices and/or client computing devices. The method further includes determining whether the change in the state triggers a new leader computing device to be selected. The method further includes initiating a new leader election among the plurality of computing devices responsive to determining the change in the state triggers the new leader computing device to be selected. The method further includes receiving an identification of the new leader computing device based on the initiating of the new leader election.

Abstract: A method of generating a synthetic training dataset for training a machine learning model using an original training dataset including a plurality of features includes selecting a feature ci of the original training dataset as a target vector yi, selecting remaining features of the original training dataset as a set of training input vectors X\i, where X\i includes all features of the training dataset other than a feature corresponding to the selected feature ci, and training a prediction model f(yi|X\i). The method generates an estimate y?i of the target vector yi by applying the prediction model to the set of training vectors X\i, and inserts a synthetic feature c?i corresponding to the estimate y?i of the target vector yi into a synthetic training dataset.

Abstract: A method performed by a central server node in a distributed machine learning environment is provided. The method includes: managing distributed machine learning for a plurality of local client nodes, such that a first set of the plurality of local client nodes are assigned to assist training of a first central model and a second set of the plurality of local client nodes are assigned to assist training of a second central model; obtaining information regarding network conditions for the plurality of local client nodes; clustering the plurality of local client nodes into one or more clusters based at least in part on the information regarding network conditions; re-assigning a local client node in the first set to the second set based on the clustering; and sending to the local client node a message including model weights for the second central model.

Abstract: An automated improving of quality of service of a data center. Transients of a power grid fed to a power supply unit are monitored by a probe. Information on transients is provided across an interface to a server of the data center. Based on characteristics of the transients, a reliability of the data center is subjected to automated updating. A request for migration of workload requiring a higher reliability than the updated reliability can be sent to a central management. When the central management has identified another data center that can meet the required reliability, the central management migrates or relocates the workload to the another data center.

Abstract: A method of performing federated feature selection for a machine learning model in a federated learning environment includes obtaining, at a first resolution, a global set of selector neural network weights. At a second resolution, the method selects, for a plurality of first data subsets, a first set of features from a feature space by iteratively applying a first selector neural network that is initialized with the global set of selector neural network weights to the first data subset to obtain a first set of selector neural network weights. The first data subsets are divided into a plurality of second data subsets, and, at a third resolution, a second set of features is selected from the feature space.

Abstract: Embodiments herein disclose, e.g., a method performed by a control network node in a communications network for handling machine learning (ML) models in the communications network. The control network node determines whether or not to transmit to a network node in the communications network a ML model based on a signature and/or a loss value of the network node, wherein the signature and/or the loss value is related to ML modelling. In case where it is determined to transmit, the control network node transmits the ML model to the network node.

Abstract: Systems and methods are provided for generating a predicted number of viewers in support of selecting an advertisement to be displayed during an advertisement break.

Abstract: A method for predicting a location of a fault in a system is described. The method includes obtaining a faulty unit pertaining to a Supervised Self-Organizing Map (SSOM), wherein the faulty unit has been derived from a sample of machine-level metrics for the system and the SSOM has been trained with a first layer of machine-level metrics and a second layer of service-level metrics. The method further includes expanding a circle originating at the faulty unit until a number of normal units of the SSOM falls within the circle, computing, for each machine-level metric, a score based on differences between the faulty and normal units, wherein a set of scores comprises the respective score for each machine-level metric, and selecting a sub-set of the set, wherein the sub-set comprises the greatest scores of the set, whereby the fault is predicted as located according to metrics represented by the sub-set.

Abstract: A method and a machine learning manager (100) for handling prediction of service characteristics using machine learning applied in a target domain (102B). A source model MS used for machine learning pre-trained in a source domain (102A) is obtained, and a transfer configuration that divides the source model into a fixed first part and a non-fixed second part is selected. A target model is created by applying the selected transfer configuration on the source model so that the target model is divided into said first and second parts. The second part is then trained using observations collected in the target domain, and the target model MT with the first part and the trained second part is provided for prediction of service characteristics in the target domain.