Wireless Communication Data Set Signaling for Private Model Training
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Solution Overview
Problem
Existing ML/AI-based wireless communication systems face challenges in model training, particularly in CSI feedback, where direct model transmission leads to privacy concerns and limits model selection diversity and performance due to restricted input and output interfaces.
Innovation Solution
A communication method where devices exchange data set information to facilitate model training without direct model transfer, allowing for flexible interface configurations and enhanced model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If models are directly transmitted between devices, then model training efficiency is improved, but privacy concerns arise and model selection diversity is limited
Solution Approach 1:
The patent extracts the model into its constituent data sets and transmits only the data sets between devices. This allows the second device to train multiple different models using the same data sets, thereby increasing model selection diversity while maintaining training efficiency. The model itself is not transmitted, only the training data, which resolves the contradiction between efficiency and diversity.
Solution Approach 2:
The patent segments the model into separate data sets (first data set for input interface, second data set for output interface). This segmentation allows the receiving device to combine these data sets with different local models, enabling multiple model variations from the same transmitted components, thus improving model selection diversity while maintaining efficient data transmission.
2Productivity
If models are directly transmitted between devices, then model training efficiency is improved, but privacy concerns arise
Solution Approach 1:
The patent extracts the sensitive model parameters and replaces them with data sets that contain only training information. By transmitting data sets instead of trained models, the patent maintains training efficiency while reducing privacy risks associated with model transmission. The data sets can be used to train multiple models without exposing the actual model structures or proprietary algorithms.
3Ease of manufacture
If fixed model interfaces are used, then implementation simplicity is improved, but adaptability to different model types deteriorates
Solution Approach 1:
The patent implements universal data set structures that can serve multiple model types. The first data set (input interface) and second data set (output interface) are designed with flexible formats that can accommodate different model architectures. This universality allows the same data set transmission mechanism to work with various model types, improving adaptability while maintaining implementation simplicity through standardized data exchange protocols.
Solution Approach 2:
The patent introduces dynamic interface configuration where the data set structures can be adapted to match different model requirements. The input and output data sets can be configured with different formats, dimensions, and types based on the specific model being trained, allowing the system to dynamically adjust to various model types while maintaining a consistent transmission framework.
Data Source
AI summary
Provided are a communication method and device. The method comprises: a first device sending first information to a second device, wherein the first information is used for indicating the type of a first data set, or a second device receiving first information transmitted by a first device, wherein the first information is used for indicating the type of a first data set.