Enterprise Neural Network Model Training via Secure Intermediary Environment
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Solution Overview
Problem
Client devices lack sufficient resources to train neural networks, and existing methods for providing pre-trained models face security and privacy challenges when using enterprise data.
Innovation Solution
A secure and private process is established to generate enterprise-specific neural network models by creating a training environment that uses authorized client data, employing virtualization and identity management to streamline the process and ensure secure data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If neural networks are trained on client devices, then model customization is improved, but device resources are exceeded
Solution Approach 1:
The system divides the training process into two separate segments: (1) initial model training performed on a server with sufficient resources, and (2) fine-tuning or adaptation performed on the client device with limited resources. This segmentation allows the heavy computational workload to be offloaded while still enabling model customization for enterprise-specific data.
2Quantity of substance
If pre-trained models are provided to client devices, then resource constraints are resolved, but security and privacy challenges increase
Solution Approach 1:
The system introduces an intermediary training environment that acts as a secure bridge between the enterprise data and the client device. Enterprise-specific data is transmitted to this controlled environment where the model is trained or fine-tuned, and only the resulting model parameters are returned to the client device. This intermediary layer protects sensitive data while enabling customization.
3Measurement precision
If enterprise data is used for training, then model accuracy is improved, but data security risks increase
Solution Approach 1:
The system performs preliminary actions by establishing secure transmission protocols and controlled access mechanisms before enterprise data is used for training. The training environment is pre-configured with security measures, and data transmission is authorized through identity management systems, ensuring that security precautions are in place before sensitive enterprise data is exposed to the training process.
Data Source
AI summary
Various examples for providing neural network models to client devices are described. A management application can cause a training environment to be created for training a neural network using enterprise data authorized by a client device. The management application can cause the client device to send the enterprise data to the training environment. The management application can cause a training application running in the training environment to create a neural network model using the enterprise data. The management application can send a neural network model to the client device.