AIML Model Control in Communication Networks
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Solution Overview
Problem
Current systems lack effective methods for controlling and managing the re-training of Artificial Intelligence and Machine Learning (AIML) models in communication networks, leading to potential performance degradation due to uncontrolled model modifications and lack of feedback coordination.
Innovation Solution
A method where a first network node transmits an AIML model to a second network node, providing information on whether the model can be re-trained or modified, and receiving notifications on any re-training or modification performed by the second network node, ensuring controlled updates and maintaining model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the second network node is allowed to re-train or modify the AIML model independently, then the model can be adapted to local conditions and improve local performance, but the first network node loses control over model consistency and multiple variant models proliferate across the network
Solution Approach 1:
The patent implements a feedback mechanism where the second network node notifies the first network node when re-training or modification is performed. This allows the first network node to maintain awareness and control of model variations across the network while still permitting local adaptation, resolving the contradiction between adaptability and consistency.
Solution Approach 2:
The first network node provides information to the second network node indicating whether the AIML model can be re-trained or modified before deployment. This preliminary action establishes control boundaries in advance, allowing local adaptation only in approved scenarios while maintaining overall model consistency.
2Stability of the object's composition
If the first network node provides information on whether the model can be re-trained or modified, then model updates are controlled and consistency is maintained, but the system complexity increases due to additional signaling and coordination
Solution Approach 1:
The patent extracts the control decision-making function from the first network node by providing explicit information about whether re-training or modification is permitted. This separates the control policy from the execution, reducing the signaling overhead compared to requiring continuous approval for each model change.
3Ease of operation
If the second network node performs re-training or modification without notification, then the system operates with simpler procedures, but the first network node cannot receive feedback or maintain control over model performance
Solution Approach 1:
The patent implements a notification mechanism where the second network node informs the first network node when re-training or modification is performed. This maintains feedback channels for performance monitoring and control while keeping the operational procedure relatively simple through event-driven notification rather than continuous communication.
Data Source
AI summary
Systems and methods to control AIML model re-training in communication networks are provided. In some embodiments, a method performed by a first network node includes transmitting a FIRST MESSAGE towards a second network node, including a model; and receiving a SECOND MESSAGE transmitted by the second network node, including an indication that the second network node has re-trained or modified the model. Some embodiments propose a method for a first network node to control whether and how an AIML model, possibly trained by a first network (or by another node), provided to a second network node could or should be re-trained or modified by the second network node. A modification of the model, such as in its structure, would implicitly require a model re-training, whereas a model re-training (or updating) does not imply a modification of the model itself, but just an optimization of the model parameters.