Edge AI Model Training via Local Weight Updates
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Solution Overview
Problem
Existing AI model training on edge devices faces challenges such as overfitting, exposure of private user data, limited communication bandwidth, and resource constraints, particularly when multiple models coexist on devices with limited compute resources.
Innovation Solution
A system with an update module on edge devices that generates and processes local training data, determines individualized weight updates, and securely transmits these updates to a remote server for global aggregation without exposing sensitive data, using encryption and optimized communication management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If training data is sent to the server for global updates, then model training accuracy is improved, but user privacy is exposed
Solution Approach 1:
The patent extracts only the essential training data characteristics needed for model improvement while leaving the actual private data on the edge device. This allows global model updates without transmitting sensitive user information to the server, resolving the contradiction between training accuracy and privacy protection.
Solution Approach 2:
The patent introduces an intermediary processing mechanism that transforms raw training data into anonymized features or gradients before transmission. This intermediary layer enables the server to perform global model updates while preventing direct exposure of user privacy, balancing both training effectiveness and data protection.
2Adaptability or versatility
If multiple models are trained on the edge device, then functionality is improved, but resource consumption increases
Solution Approach 1:
The patent segments the training process into local edge device training for multiple models and centralized server aggregation. This allows multiple models to coexist on the edge device for enhanced functionality while distributing the computationally intensive aggregation work to the server, reducing overall device power consumption.
3Reliability
If model weights are updated frequently, then model performance is improved, but communication bandwidth is consumed
Solution Approach 1:
The patent extracts only the essential weight update information needed for model improvement and transmits only these critical updates to the server. This selective transmission maintains model performance while significantly reducing the amount of data communicated over the network.
4Object-affected harmful factors
If local training is performed on the edge device, then user privacy is protected, but model generalization deteriorates
Solution Approach 1:
The patent merges local edge device training with centralized server aggregation by combining locally trained model parameters through federated averaging or similar techniques. This integration allows the model to benefit from both local privacy-preserving training and global knowledge aggregation, improving generalization while maintaining user privacy.
Solution Approach 2:
The patent implements a feedback loop where locally trained models send updates to the server, which aggregates these updates and returns improved global model parameters to the edge devices. This iterative feedback process enables continuous model improvement and better generalization while keeping training data localized.
Data Source
AI summary
A system includes an update module residing on an edge device. The update module receives training data for a model corresponding to an application. The model and the application are on the edge device. Training data specific to the edge device is generated at the edge device. The update module provides the training data to a learning system. The learning system is on the edge device and determines individualized weight updates for the model using the training data. The update module receives, from the learning system, the individualized weight updates and provides, to a remote server, the individualized weight updates. The remote server determines global weight updates based on the individualized weight updates and other individualized weight updates from other edge device(s). The edge device receives weight updates that are based on the global weight updates, Thus, training data generated on the edge devices remains on the edge device.