Network Node ML Model Handover via Signature and Loss Matching
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Solution Overview
Problem
Existing methods for training machine learning models in dynamic network environments are time-consuming, resource-intensive, and face challenges with data availability and privacy issues, particularly in scenarios where network nodes experience changes or additions, leading to inefficient model training and performance degradation.
Innovation Solution
A method for seamless handover of pre-trained machine learning models between network nodes using similarity checks based on model signatures and loss values, enabling rapid inference and adaptation through transfer learning and fine-tuning, without the need for extensive data transfer or initial training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained from scratch for each network node, then model accuracy and reliability are improved, but training time and resource consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on a source network node before deployment. The model is trained in advance using historical data and performance metrics from the source node, then transferred to a target node. This eliminates the need for training from scratch at each node, significantly reducing training time while maintaining model accuracy through transfer learning and fine-tuning with local data.
Solution Approach 2:
The patent implements copying by creating a replica of the pre-trained machine learning model from the source network node and deploying it to the target network node. Instead of training a new model from scratch, the system copies the trained model weights, architecture, and parameters, then adapts them to the target node's specific conditions through fine-tuning with local performance data and KPIs.
2Adaptability or versatility
If machine learning models are trained from scratch for each network node, then model adaptability to specific node conditions is improved, but resource consumption and energy usage increase
Solution Approach 1:
The patent applies preliminary action by performing the computationally intensive training phase on a source network node with sufficient resources before deployment. The pre-trained model is then transferred to target nodes with limited resources, which only require lightweight fine-tuning using local data. This approach maintains model adaptability to specific node conditions while dramatically reducing energy consumption at the target nodes.
Solution Approach 2:
The patent implements copying by transferring the pre-trained model from the source node to the target node. This allows the target node to leverage the source node's training resources while adapting the model to local conditions through fine-tuning with node-specific performance data, KPIs, and traffic patterns, thereby reducing local energy consumption while maintaining adaptability.
3Reliability
If training data is collected from multiple network nodes, then model training quality is improved, but data privacy issues and bandwidth consumption increase
Solution Approach 1:
The patent introduces an intermediary approach by using a pre-trained model as a mediator that captures general patterns from the source node's data without requiring direct data sharing. The model serves as an intermediary representation that can be transferred and adapted to target nodes, enabling knowledge transfer while maintaining data privacy and reducing bandwidth consumption compared to raw data transfer.
Solution Approach 2:
The patent applies copying by transferring the trained model parameters and weights from the source node to target nodes instead of copying actual training data. This approach allows target nodes to benefit from the source node's training quality and data patterns while avoiding data privacy issues and bandwidth consumption associated with transferring raw training datasets.
4Reliability
If machine learning models are retrained when network nodes are added or changed, then model performance is maintained, but training costs and time consumption increase
Solution Approach 1:
The patent applies preliminary action by pre-training models on source network nodes before deployment to target nodes. When new nodes are added or existing nodes change, the system can quickly deploy pre-trained models and perform rapid fine-tuning with local data, avoiding the need for complete retraining. This maintains model performance while significantly improving deployment efficiency and reducing training costs.
Solution Approach 2:
The patent implements copying by replicating pre-trained models from source nodes to target nodes when network topology changes occur. This allows rapid deployment to new nodes without retraining from scratch, maintaining model performance through transfer learning while improving productivity and reducing the time and cost associated with model deployment and updates.
Data Source
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AI summary
Embodiments herein disclose e.g. a method performed by a control network node (14) 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 (10,12) in the communications network a ML model based on a signature and/or a loss value of the network node (10,12), wherein the signature and/or the loss value is related to ML modelling. In case determined to transmit, the control network node transmit the ML model to the network node.