Multi-Vendor Sequential Training for Wireless ML Interoperability
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Solution Overview
Problem
The challenge in wireless communications systems is the proprietary nature of machine learning encoders and decoders, which hinders seamless integration and coordination between different vendors, leading to issues in training and deployment due to the proprietary information associated with neural network models and training data.
Innovation Solution
A multi-vendor sequential training approach is implemented, where a first network entity receives control information from a second network entity, trains a machine learning encoder and decoder, and transmits a decoder sequential training dataset to facilitate training of a second machine learning encoder, allowing for interoperability across different vendor systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If proprietary machine learning encoders and decoders are used by different vendors, then each vendor can protect their intellectual property and maintain competitive advantage, but seamless integration and coordination between different vendors becomes hindered
Solution Approach 1:
The patent introduces a neutral third-party server as an intermediary that coordinates the training process between competing vendors. The server facilitates the exchange of training data and model parameters without requiring vendors to share their proprietary complete models. This mediator enables collaboration while preserving each vendor's intellectual property, resolving the contradiction between proprietary protection and interoperability.
Solution Approach 2:
The patent segments the machine learning model training process into separate stages that can be performed independently by different vendors. Instead of requiring complete model sharing, the training is divided into phases where each vendor contributes specific components or training iterations. This segmentation allows vendors to maintain control over their proprietary elements while still achieving integrated multi-vendor functionality.
2Adaptability or versatility
If complete model sharing is implemented for coordinated training, then seamless integration between vendors is achieved, but proprietary information about neural network models and training data is exposed
Solution Approach 1:
The patent extracts only the essential training data and model parameters needed for coordination, while leaving out the complete proprietary models and sensitive training datasets. By taking out only what is necessary for the training coordination function, the system achieves vendor interoperability without exposing sensitive proprietary information that would be present in complete model sharing.
Solution Approach 2:
The neutral server acts as an intermediary that handles the exchange of training information between vendors. It receives training data and model parameters from one vendor, processes them appropriately, and transmits relevant information to another vendor without either vendor needing to have direct access to the other's complete proprietary models. This mediator mechanism enables coordination while preventing proprietary information exposure.
3Reliability
If sequential training approach is used where one vendor trains first then another, then proprietary information protection is maintained, but training coordination and interoperability optimization is reduced
Solution Approach 1:
The patent implements preliminary actions where the first vendor completes their training phase and prepares training data before the second vendor begins their training. This structured sequencing ensures proprietary information is protected during each phase while still enabling effective coordination. The preliminary completion of one vendor's training provides a foundation for the next vendor's training, maintaining both security and efficiency.
Solution Approach 2:
The sequential training approach incorporates feedback mechanisms where the output or performance metrics from the first vendor's trained model inform the second vendor's training process. This feedback loop allows the second vendor to optimize their model based on the first vendor's results, maintaining training efficiency and interoperability optimization without requiring simultaneous access to all proprietary information.
Data Source
AI summary
An apparatus, method and computer-readable media are disclosed for performing wireless communications. An example method of wireless communications at a first network entity associated with a first vendor includes receiving, at the first network entity, control information from at least a second network entity associated with at least a second vendor, training a first machine learning encoder and a machine learning decoder at the first network entity using the control information and transmitting, to at least the second network entity, a first decoder sequential training dataset for use in training a second machine learning encoder at the second network entity, the first decoder sequential training dataset including at least one of the control information or a latent representation of the control information output by the first machine learning encoder.