Local AI Model Training for Wireless Communication Security
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Solution Overview
Problem
Current wireless communication systems face challenges in providing personalized AI experiences for user equipment (UE) due to privacy and data volume concerns, leading to generic AI modules that do not optimize user experience, as UE cannot report all information to the network and network-side AI training results in one-size-fits-all solutions.
Innovation Solution
A method where the UE performs operations based on AI prediction results that satisfy network configurations, allowing for localized AI model training and data processing, reducing data leakage and improving security while customizing AI experiences for each UE.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If UE reports all information to the network for AI training, then network-side AI can be improved, but user privacy and data security are compromised
Solution Approach 1:
The patent extracts the AI model training function from the network side and relocates it to the UE side. The UE locally trains the AI model using its own data, extracting only the necessary model parameters or predictions for network communication. This extraction approach maintains AI accuracy while eliminating the security risk of transmitting sensitive user data to the network.
Solution Approach 2:
The patent implements local AI model training at the UE, allowing each UE to customize its AI model according to its specific characteristics and data. This local化处理 enables personalized AI services while keeping sensitive data confined to the local device, thus resolving the contradiction between AI accuracy and data security.
2Productivity
If network trains common AI modules for all UEs, then network resource efficiency is improved, but user experience becomes generic and not optimized
Solution Approach 1:
The patent segments the AI training process into two parts: common knowledge that can be shared and user-specific data that remains local. The network can train common AI models efficiently, while each UE independently fine-tunes the model using its local data, achieving both network efficiency and user personalization.
Solution Approach 2:
The patent enables UEs to autonomously train and optimize their own AI models using local data without requiring network intervention for each training iteration. This self-service approach allows continuous personalization while reducing network load, resolving the contradiction between network efficiency and AI adaptability.
3Object-affected harmful factors
If UE processes AI data locally, then data security is improved, but device computational load increases
Solution Approach 1:
The patent implements partial local processing where the UE performs AI model training and data processing only when locally configured to do so, rather than continuously. The network can control when local processing is necessary, balancing data security requirements with energy consumption constraints of the mobile device.
Data Source
AI summary
A method and a device for communication processing are disclosed. The method includes: triggering a UE to perform an operation corresponding to a network configuration in response to an AI prediction result satisfying the network configuration.