EP4635145A1 - Procédés, noeud central et noeud périphérique pour l'entraînement d'un modèle de réseau neuronal graphique (gnn) par apprentissage automatique fédéré (fml), pour une évaluation de performance de réseau dans un grand réseau - Google Patents

Procédés, noeud central et noeud périphérique pour l'entraînement d'un modèle de réseau neuronal graphique (gnn) par apprentissage automatique fédéré (fml), pour une évaluation de performance de réseau dans un grand réseau

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Publication number
EP4635145A1
EP4635145A1 EP22835890.9A EP22835890A EP4635145A1 EP 4635145 A1 EP4635145 A1 EP 4635145A1 EP 22835890 A EP22835890 A EP 22835890A EP 4635145 A1 EP4635145 A1 EP 4635145A1
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EP
European Patent Office
Prior art keywords
network
node
gnn
model
local
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EP22835890.9A
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German (de)
English (en)
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Zhongwen Zhu
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Telefonaktiebolaget LM Ericsson AB
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Telefonaktiebolaget LM Ericsson AB
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Publication of EP4635145A1 publication Critical patent/EP4635145A1/fr
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0893—Assignment of logical groups to network elements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence

Definitions

  • the present disclosure relates to a graph neural network (GNN) based federated machine learning solution for network operation performance prediction in a large operator network.
  • GNN graph neural network
  • NMS network management system
  • GNN graph neural network
  • each node in the network has its own neural network architecture. Due to different numbers of neighbor nodes, the GNN architecture for one node might be totally different from that for another node. But the parameters given for message passing are the same among all the nodes. Hence, the model training can be done on a subset of nodes (either randomly picked or targeted) in the network.
  • GNN One of the advantages of GNN is that the trained model can be directly applied to those nodes that are not seen in the training process.
  • GNN is a good fit for building an AI/ML model that captures network performance information from the datasets.
  • the trained model can detect if the network performance is “normal” or “abnormal”.
  • FML federated machine learning
  • the GNN model training can be dynamically pushed to the edge node, which is closer to the local network. This reduces the network bandwidth usage required for transferring the datasets from the network nodes to the model training node, i.e., the edge node.
  • a computer implemented method executed in a central node, for training a graph neural network (GNN) model through federated machine learning (FML), for network performance assessment within a large network.
  • the method comprises defining at least two local networks within the large network.
  • the method comprises selecting one edge node for each of the at least two local networks for training a local GNN model.
  • the method comprises sending, to each edge node, a network topology for the local network, the local GNN model selected, and information about nodes in the local network.
  • the method comprises sending initial weights for the local GNN model to each edge node and receiving updated weights from each edge node.
  • the method comprises updating the GNN model using the updated weights received from each edge node, using FML.
  • a computer implemented method executed in an edge node, for training a graph neural network (GNN) model through federated machine learning (FML), for network performance assessment within a large network.
  • the method comprises receiving a network topology for a local network, a GNN model with initial weights and information about nodes in the local network, from a central node.
  • the method comprises acquiring datasets from nodes in the local network.
  • the method comprises verifying and labelling the datasets.
  • the method comprises training the GNN model using the datasets.
  • the method comprises sending updated weights to the central node.
  • a central node for training a graph neural network (GNN) model through federated machine learning (FML), for network performance assessment within a large network.
  • the central node comprises processing circuits and a memory, the memory containing instructions executable by the processing circuits whereby the central node is operative to define at least two local networks within the large network.
  • the central node is operative to select one edge node for each of the at least two local networks for training a local GNN model.
  • the central node is operative to send, to each edge node, a network topology for the local network, the local GNN model selected, and information about nodes in the local network.
  • the central node is operative to send initial weights for the local GNN model to each edge node and receive updated weights from each edge node.
  • the central node is operative to update the GNN model using the updated weights received from each edge node, using FML.
  • an edge node for training a graph neural network (GNN) model through federated machine learning (FML), for network performance assessment within a large network.
  • the edge node comprises processing circuits and a memory, the memory containing instructions executable by the processing circuits whereby the edge node is operative to receive a network topology for a local network, a GNN model with initial weights and information about nodes in the local network, from a central node.
  • the edge node is operative to acquire datasets from nodes in the local network.
  • the edge node is operative to verify and label the datasets.
  • the edge node is operative to train the GNN model using the datasets.
  • the edge node is operative to send updated weights to the central node.
  • a non-transitory computer readable media having stored thereon instructions for training a graph neural network (GNN) model through federated machine learning (FML) for network performance assessment within a large network.
  • the instructions comprise any of the steps described herein.
  • Figure 1 is a schematic illustration of the overview of the proposed solution.
  • Figure 2 is a block diagram of the component view of the central management system.
  • Figure 3 is a flowchart of the logical flow in the GFML master.
  • Figure 4 is a flowchart of the logical flow in the GFML Client.
  • Figure 5 is a block diagram of two-layer GNN architecture with local network for node Ri.
  • Figure 6 is a block diagram of two-layer GNN architecture with local network for node Ci and R2.
  • Figure 7a is a flowchart of a method, executed in a central node, for training a graph neural network (GNN) model through federated machine learning (FML), for network performance assessment within a large network.
  • GNN graph neural network
  • FML federated machine learning
  • Figure 7b is a flowchart of a method, executed in an edge node, for training a graph neural network (GNN) model through federated machine learning (FML), for network performance assessment within a large network.
  • GNN graph neural network
  • FML federated machine learning
  • Figure 8 is a schematic illustration of a hardware in which steps and/or method described herein can be executed.
  • Figure 9 is a schematic illustration of a virtualization environment in which the different methods and apparatuses described herein can be deployed.
  • computer readable carrier or carrier wave may contain an appropriate set of computer instructions that would cause a processor to carry out the techniques described herein.
  • At least some aspects of the techniques described herein may be implemented using artificial intelligence, which comprises a variety of techniques as would be apparent to a person skilled in the art, including machine learning techniques.
  • Machine learning techniques include neural network (NN), or artificial neural network (ANN), and both terms may be used interchangeably herein.
  • an artificial neural network could include biological portions.
  • a GNN-based FML solution for industrial applications, such as for monitoring network performance, in an edge cloud infrastructure is disclosed herein.
  • the solution addresses how to select a subset of the network for local training, how to select an edge node to serve as a GNN training node, how to do the node embedding, including network representation at edge node, how to exchange the model among the edge nodes for building a global GNN model.
  • the proposed solution introduces a mechanism to select local, or subset of, networks 105 within the large target network for building the global model.
  • the edge nodes 110 selected for training the local GNNs are dynamically located in order to be closer to the target local networks and to the nodes 125. As shown in Figure 1, edge node 1 (on the left) is assigned to local network 1, edge node k (on the right) is assigned to local network k.
  • the selection can be done either in a random fashion or following a given distribution.
  • the selection can also be done through a rule/policy that is defined or configured according to an analytic report.
  • the computational nodes deployed at the edge that are selected for training the GNN models can be selected to optimize: an average cost to transfer the dataset from local network nodes to the edge nodes;
  • the footprint of the selected GNN model i.e. the size of the memory required to execute the GNN model.
  • the local networks information (node + network topology) is provided to the selected edge nodes (e.g. via instructions) so that the edge nodes can pull the datasets from the network nodes.
  • the instructions sent to the edge nodes might include settings for advanced options, such as: specifying features or key performance indicators (KPIs) that the edge nodes should request from the network nodes; and a turn on, turn off schedule, that specify when to generate the required data/features at the network node.
  • KPIs key performance indicators
  • the GNN model proposed herein is built for simple binary classification at network level (network performance is normal vs. abnormal). The mechanism can be easily extended to other classification.
  • GFML4NM-CMS Central Management System
  • GFML4NM-Client 120 The other component is deployed to the edge platform closer to the local network to be targeted, it is a GFML4NM-Client 120. [0041] The detailed view on those two components is given in figure 2.
  • Central management system GFML4NM-CMS 115 consists of four modules, in addition to storage 215:
  • GFML Master 225 is responsible for selecting the edge nodes for the target local networks, and also to select the GNN model.
  • the GFML Master 225 provides the advanced options mentioned above to the edge nodes 110.
  • the GFML Master 225 sends the instruction to the edge nodes and monitors the status of all the edge nodes involved in the FML.
  • - FML module 220 deals with the basics of federated machine learning, such as aggregating the model weights collected from different edge nodes.
  • - DX4FML module 205 oversees data exchanging for federated machine learning. This module is responsible for exchanging the model data under the federated machine learning framework between GFML4NM clients 120 and the central management system 115, GFML4NS-CMS, including data synchronization, and any other relevant data.
  • DPM4FML module 210 is the data processing module for federated machine learning. This module is responsible for preparing the data for the federated machine learning, including, for example, cleaning and repairing data, and any other relevant data processing.
  • the client, GFML4NM-Client 120, consists of four modules, in addition to storage 240:
  • the GFML Agent 250 is responsible for receiving the instruction from the GFML Master 225.
  • the instruction includes the target local network (nodes and network topology), the GNN model and corresponding node level data/features, selected by the GFML Master, for harvesting at the network nodes.
  • GNN Module 245, or graph neural network module is the framework to host different GNN models, which are trained using the dataset collected from the network nodes in target local networks via the data collection module (DCM).
  • DCM data collection module
  • DPM Data processing module
  • FIG. 3 is a flowchart illustrating the logical flow 300 executed in the GFML master 225 to select the edge nodes.
  • the flow starts at step 301, with the selection of several target locations to build the network performance classification model based on pre-defined rules. This can be based on random selection.
  • the GFML master 225 selects the model to be considered (e.g., GNN, etc.).
  • the GFML master 225 builds up data quality verification rules according to the selected model.
  • the data quality verification rules can include a minimum number of nodes in the network, the size of the datasets, balancing ratios for the datasets, normalization of the data, labelling criteria, etc.
  • the GFML master 225 locates the edge nodes closely deployed in or next to the target local network, which is defined through the GFML master 225.
  • the GFML master 225 sends instructions for dataset preparation to the edge nodes, including the data quality verification rules and the target local network topologies.
  • the GFML master 225 waits for responses from the target edge nodes.
  • steps 307 to 311 or steps 312 to 314 and 311 are executed. These steps may occur in parallel for different responses of different edge nodes.
  • the GFML master 225 receives a response indicating dataset readiness from an edge node.
  • the GFML master 225 checks if all the edge nodes provided a response. If all responses are not received, the GFML master 225 return to the waiting stage, step 306. If the GFML master 225 has received a response from all the edge nodes, at step 309, the GFML master 225 checks if the initial setup for FML is ready. If not ready, the GFML master 225 return to the waiting stage, step 306. If ready, step 310, the GFML master 225 sets up the initialization weights for the selected model. At step 311, the GFML master 225 sends the model weights to the edge nodes. In the initialization stage, the initialization weights are provided.
  • the GFML master 225 receives updated model weights from the edge nodes.
  • the GFML master 225 checks if all the edge nodes provided the updated model weights. If all edge nodes have not provided the updated model weights, the GFML master 225 return to the waiting stage, step 306. If yes, at step 314, FML is applied to update the global model. Then the GFML master 225, sends, at step 311, the model weights to the edge nodes
  • Federated learning one common algorithm that can be selected is the federated averaging algorithm, which is also called “FedAvg”. It is the very first vanilla federated learning algorithm developed by Google®. The corresponding algorithm is provided below, as reference. Basically, it averages the model weights from randomly selected clients, which participate in the global model training process.
  • B is the local minibatch size
  • E is the number of local epochs
  • r is the learning rate
  • Figure 4 is a flowchart illustrating the logical flow executed in the GFML Client 120, which runs in the edge nodes 110.
  • the flow starts at step 401, with the GFML Client 120, running in the edge node 110, receiving instructions from the GFML Master 225, or central server, to prepare certain amount of data, the instructions including data quality verification rules.
  • the GFML Client 120 collects the data from the nodes 125 in the target local network 105 with which it is associated, for training the model.
  • the GFML Client 120 builds the node embedding, described further down in details, and network representation using both node features and network topology.
  • the GFML Client 120 normalizes the dataset acquired from the nodes 125 using standard deviation.
  • the GFML Client 120 labels the dataset by applying predetermined criteria such as packet loss rate, retransmission rate, etc. for binary classification (normal or abnormal network performances. Then, at step 406, the GFML Client 120 verifies that the amount of data that qualifies corresponds to the amount of data requested by the GFML master 225.
  • the GFML Client 120 If the amount of data is not sufficient, the GFML Client 120 returns to the collection of data step, 402. If yes, the GFML Client 120 notifies the GFML master 225 on the readiness of the dataset (this can take the form of a success or failure indication), step 408.
  • the GFML Client 120 transitions to a waiting state where it waits for instructions or for a model update from the GFML Master 225.
  • the GFML Client 120 can receive instructions to acquire further data (returning to step 401), or the GFML Client 120 can receive the global model (initial or updated model weights) from the GFML Master 225, step 410.
  • the GFML Client 120 updates the GNN model it keeps locally, step 411.
  • the GFML Client 120 selects a subset of the dataset acquired locally, step 412, and trains the local GNN model using the selected subset of the dataset, for local network operation performance classification, step 413.
  • the GFML Client 120 checks if the stop training criteria are met. If it is determined that more training needs to be done, the GFML Client 120 returns to the step 412. If the training is sufficient to meet the criteria, the GFML Client 120 retrieves the model weights and sends the weights to the GFML Master 225, step 415. Then it returns to the waiting state, step 409.
  • the GFML client 120 collects the dataset from the nodes 125 in the target the local network 105. It does the node embedding and network representation by using node features plus the network topology received from the GFML master 225.
  • GFML4NM-CMS central management server
  • GFML4NM-Client 120 which is deployed in the edge nodes 110 closer to the potential target networks 105, referring to figures 1 and 2.
  • the central management server verifies the size of the target network and decides if the target network should be broken into several small networks 105 to apply the proposed GNN based FML solution or not.
  • the default value of the network size can be selected based on experimental results obtained from either design lab or test net in an operator’s network, for example the default value could be one hundred network nodes.
  • Each layer contains both a message 505 and an aggregation parts 510.
  • Each node 125 (Ri, R2, R3) in the local network has its own GNN architecture. Three nodes are used to demonstrate how their GNN architectures look like.
  • Ri is taken as a target node 125.
  • the router R2 For the first layer, it has five neighbors, one is the router R2, the other four instances are cells Ci to C4 located in different places.
  • the cell nodes For the second layer, the cell nodes have only one neighbor, Ri.
  • the inputs for layer 2 are all the routers, in this example.
  • cell Ci has only one neighbor node at the first layer, Ri, and five nodes at second layer, Ci, C2, C3, C4 and R2.
  • FIG. 6 (b) the GNN architecture is shown for router R2, which has two nodes Ri and R3 at the first layer and total seven nodes at the second layer, with Ci, C2, C3 C4 and R2 neighboring Ri and C5 and R2 neighboring R3.
  • Algorithm 1 GraphSAGE embedding generation (i.e., forward propagation) algorithm
  • Input Graph G (V, E); input features ⁇ xy , Vv G V ⁇ ; depth K ; weight matrices
  • KPIs and counters are commonly considered: network bandwidth (BW), data re-transmission ratio (DRR), packet loss ratio (PLR), central processing unit (CPU) load or usage, memory (Mem) usage.
  • BW network bandwidth
  • DRR data re-transmission ratio
  • PLR packet loss ratio
  • CPU central processing unit
  • Mem memory usage
  • h u ⁇ CPU, Mem, BW, DRR, PLR, ... ⁇ , (1) where h u represents a network node u.
  • the node embedding z u can be built up after L layers by following the multi-layer GNN architecture referring to equations (2) and (3).
  • the representation of the network can be built using a simple mean function.
  • v is the representation of a network node next to the node w
  • W represents the weights in the GNN model
  • B represents the bias in the GNN model
  • K is the number of layers
  • Z u is the representation of node u after an L-layer message passing from its neighbor nodes.
  • an auto-labelling mechanism based on given criteria is used.
  • One example of the auto-labelling rules is described hereafter. If one or more KPIs measured on the nodes in the target local network exceed a given threshold, the network performance is considered to be “abnormal”, vice versa, it will be considered “normal”. For instance, the following thresholds can be applied for labelling the network performance.
  • the packet loss ratio exceeds 80% (PLR).
  • the values for the thresholds should be configurable.
  • the graph representation yGraph as shown in equation (4), can be used and a Multi-layer Perceptron (MLP) can be applied on it using SoftMax; then the predication can be done.
  • MLP Multi-layer Perceptron
  • This loss function is used to train the GNN model for network operation performance classification.
  • Stochastic gradient descent (SDG) can be used to minimize the loss. When the loss is minimized or the model training converges, the network performance characteristics is well captured by the GNN model.
  • the proposed solution described herein provides a mechanism to train a network performance classification model by combining GNN and FML techniques efficiently.
  • FIG. 7a illustrates a method 700, executed in a central node, for training a graph neural network (GNN) model through federated machine learning (FML), for network performance assessment within a large network
  • GNN graph neural network
  • FML federated machine learning
  • the method comprises defining, step 705, at least two local networks within the large network.
  • the method comprises selecting, step 710, one edge node for each of the at least two local networks for training a local GNN model.
  • the method comprises sending, step 715, to each edge node, a network topology for the local network, the local GNN model selected, and information about nodes in the local network.
  • the method comprises sending, step 720, initial weights for the local GNN model to each edge node and receiving updated weights from each edge node.
  • the method comprises updating, step 725, the GNN model using the updated weights received from each edge node, using FML.
  • the GNN model may be selected among binary classification models or multiclassification models.
  • the method may further comprise defining data quality verification rules, according to the selected GNN model and sending the data quality verification rules to each edge node, the data quality verification rules being for use by each edge node to verify data quality of acquired datasets.
  • the data quality verification rules may comprise any one or more among: a minimum number of nodes in the local network from which to obtain data, a size of a dataset to acquire from each node, balancing ratios for the datasets, rules for normalization of data of the dataset, labelling criteria for data of the dataset.
  • the method may further comprise sending timing information to each edge node for controlling training cycles in each edge node.
  • the timing information may further comprise a schedule to turn on and turn off generation of datasets at the network node level and collection of the dataset by the edge node.
  • the method may further comprise, after updating the GNN model, sending updated weights for the local GNN models to the edge nodes and receiving updated weights from the edge nodes.
  • Figure 7b illustrates a method 750, executed in an edge node, for training a graph neural network (GNN) model through federated machine learning (FML), for network performance assessment within a large network.
  • the method comprises receiving, step 755, a network topology for a local network, a GNN model with initial weights and information about nodes in the local network, from a central node.
  • the method comprises acquiring, step 760, datasets from nodes in the local network.
  • the method comprises verifying and labelling, step 765, the datasets.
  • the method comprises training, step 770, the GNN model using the datasets.
  • the method comprises sending, step 775, updated weights to the central node.
  • the method may further comprise receiving the data quality verification rules from the central node for verifying the datasets.
  • the data quality verification rules may comprise any one or more among: a minimum number of nodes in the local network from which to obtain data, a size of a dataset to acquire from each node, balancing ratios for the datasets, rules for normalization of data of the dataset, labelling criteria for data of the dataset.
  • a subset of the datasets may be selected for training the GNN model.
  • the method may further comprise receiving timing information from the central node, for controlling training cycles.
  • the timing information may further comprise a schedule to turn on and turn off generation of datasets at the network node level.
  • the method may further comprise receiving updated weights from the central node and updating the GNN model.
  • HW hardware
  • the hardware 801 may be a server in the cloud or in any other type of network, a network node, a radio base station, an edge node or another computing device which may be part of a cloud computing system, edge computing system, or which, in some cases, may be a standalone device.
  • the hardware 801 when it executes the central management system is operative set up target local networks for creating a model, to determine or predict network performance, using federated learning, as explained previously.
  • the hardware 801 when it is an edge node is operative to receive instructions as previously explained to get data and train a model as well as to perform inference using the model.
  • the hardware 801 comprises processing circuitry 803 and memory 805.
  • the memory 805 can contain instructions executable by the processing circuitry 803 whereby functions and steps described herein may be executed to provide any of the relevant features and benefits disclosed herein.
  • the hardware 801 may also include non-transitory, persistent, machine-readable storage media 807 having stored therein software and/or instruction 809 executable by the processing circuitry 803 to execute functions and steps described herein.
  • the hardware may also include network interface(s) and a power source.
  • the instructions 809 may include a computer program for configuring the processing circuitry 803.
  • the computer program may be stored in a physical memory local to the device, which can be removable, or it could alternatively, or in part, be stored in the cloud.
  • the computer program may also be embodied in a carrier such as an electronic signal, optical signal, radio signal, or computer readable storage medium.
  • FIG 9 there is provided a virtualization environment 900 in which functions and steps described herein can be implemented.
  • the virtualization environment 900 may comprise systems, networks, servers, nodes, devices, etc., that are in communication with each other either through wire or wirelessly, e.g., through a network interface component (NIC) comprising physical network interface(s).
  • NIC network interface component
  • Some or all of the functions and steps described herein may be implemented as one or more virtual components (e.g., via one or more applications, components, functions, virtual machines, containers, etc.) executing on one or more physical apparatus in one or more networks, systems, environment, etc.
  • a virtualization environment provides hardware 901 comprising processing circuitry 903 and memory 905.
  • the memory 905 can contain instructions executable by the processing circuitry 903 whereby functions and steps described herein may be executed to provide any of the relevant features and benefits disclosed herein.
  • the hardware 901 may also include non-transitory, persistent, machine-readable storage media 907 having stored therein software and/or instruction 909 executable by the processing circuitry 903 to execute functions and steps described herein.
  • the instructions 909 may include a computer program for configuring the processing circuitry 903.
  • the computer program may be stored in a removable memory, such as a portable compact disc, portable digital video disc, or other removable media.
  • the computer program may be stored in a physical memory local to the hardware 901, which can be removable, or it could alternatively, or in part, be stored in the cloud.
  • the computer program may also be embodied in a carrier such as an electronic signal, optical signal, radio signal, or computer readable storage medium.
  • a central node 801, 901 for training a graph neural network (GNN) model through federated machine learning (FML), for network performance assessment within a large network.
  • the central node comprises processing circuits 803, 903, and a memory, 805, 905.
  • the memory contains instructions executable by the processing circuits whereby the central node is operative to define at least two local networks within the large network.
  • the central node is operative to select one edge node for each of the at least two local networks for training a local GNN model.
  • the central node is operative to send, to each edge node, a network topology for the local network, the local GNN model selected, and information about nodes in the local network.
  • the central node is operative to send initial weights for the local GNN model to each edge node and receive updated weights from each edge node.
  • the central node is operative to update the GNN model using the updated weights received from each edge node, using FML.
  • the GNN model may be selected among binary classification models or multi-classification models.
  • the central node may be further operative to define data quality verification rules, according to the selected GNN model and send the data quality verification rules to each edge node, the data quality verification rules being for use by each edge node to verify data quality of acquired datasets.
  • the data quality verification rules may comprise any one or more among: a minimum number of nodes in the local network from which to obtain data, a size of a dataset to acquire from each node, balancing ratios for the datasets, rules for normalization of data of the dataset, labelling criteria for data of the dataset.
  • the central node may be further operative to send timing information to each edge node for controlling training cycles in each edge node.
  • the timing information may further comprise a schedule to turn on and turn off generation of datasets at the network node level and collection of the dataset by the edge node.
  • the central node may be further operative to, after updating the GNN model, send updated weights for the local GNN models to the edge nodes and receiving updated weights from the edge nodes.
  • an edge node 801 for training a graph neural network (GNN) model through federated machine learning (FML), for network performance assessment within a large network.
  • the edge node comprises processing circuits 803 and a memory 805, the memory containing instructions executable by the processing circuits whereby the edge node is operative to receive a network topology for a local network, a GNN model with initial weights and information about nodes in the local network, from a central node.
  • the edge node is operative to acquire datasets from nodes in the local network.
  • the edge node is operative to verify and label the datasets.
  • the edge node is operative to train the GNN model using the datasets.
  • the edge node is operative to send updated weights to the central node.
  • the edge node may be further operative to receive the data quality verification rules from the central node for verifying the datasets.
  • the data quality verification rules may comprise any one or more among: a minimum number of nodes in the local network from which to obtain data, a size of a dataset to acquire from each node, balancing ratios for the datasets, rules for normalization of data of the dataset, labelling criteria for data of the dataset.
  • a subset of the datasets may be selected for training the GNN model.
  • the edge node may be further operative to receive timing information from the central node, for controlling training cycles.
  • the timing information may further comprise a schedule to turn on and turn off generation of datasets at the network node level.
  • the edge node may be further operative to receive updated weights from the central node and updating the GNN model.
  • the instructions may comprise any of the steps described herein.

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  • Data Exchanges In Wide-Area Networks (AREA)

Abstract

La divulgation concerne un procédé, un nœud central et un nœud périphérique, pour l'entraînement d'un modèle de réseau neuronal graphique (GNN) par apprentissage automatique fédéré (FML) pour une évaluation de performance de réseau dans un grand réseau. Le procédé comprend une étape consistant à définir au moins deux réseaux locaux au sein du grand réseau. Le procédé comprend une étape consistant à sélectionner un nœud périphérique pour chacun des au moins deux réseaux locaux pour l'entraînement d'un modèle GNN local. Le procédé comprend les étapes consistant à envoyer, à chaque nœud périphérique, une topologie de réseau pour le réseau local, le modèle GNN local sélectionné, et des informations concernant des nœuds dans le réseau local. Le procédé comprend les étapes consistant à envoyer des poids initiaux pour le modèle GNN local à chaque nœud périphérique et à recevoir des poids mis à jour depuis chaque nœud périphérique. Le procédé comprend l'étape consistant à mettre à jour le modèle GNN à l'aide des poids mis à jour reçus de chaque nœud périphérique, à l'aide d'un FML.
EP22835890.9A 2022-12-12 2022-12-12 Procédés, noeud central et noeud périphérique pour l'entraînement d'un modèle de réseau neuronal graphique (gnn) par apprentissage automatique fédéré (fml), pour une évaluation de performance de réseau dans un grand réseau Pending EP4635145A1 (fr)

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CN119883660B (zh) * 2025-03-28 2025-06-10 山东大学 融合图神经和混合专家模型的边缘模型调度方法及系统

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WO2020115273A1 (fr) * 2018-12-07 2020-06-11 Telefonaktiebolaget Lm Ericsson (Publ) Prédiction de performances de communication d'un réseau à l'aide d'un apprentissage fédéré
US20230177349A1 (en) * 2020-06-01 2023-06-08 Intel Corporation Federated learning optimizations
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