Meta-Learning Model Sharing Across Private Data Sets
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Solution Overview
Problem
The development of high-performing deep learning models is hindered by the inability to access proprietary data sets, which are often kept private due to privacy and proprietary concerns, limiting the sharing and generalization of models trained on these data sets, and posing challenges for teacher/student frameworks that rely on access to training data.
Innovation Solution
A process and system that evolves and trains teacher and student models on private data sets while maintaining data privacy, using a candidate suggestion service and candidate evaluation system across firewalls to determine performance metrics without exposing secure data, allowing the sharing of model performance without sharing the data itself, and enabling knowledge distillation across different data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are trained on proprietary data sets to improve model performance, then model accuracy and generalization are improved, but data privacy and proprietary protection are compromised
Solution Approach 1:
The patent introduces a firewall as an intermediary component that separates the training data environment from the model evaluation environment. The firewall allows model parameters and performance metrics to pass through while blocking access to the actual training data, thus enabling model improvement without compromising data privacy. This mediator resolves the contradiction by facilitating model accuracy improvement while maintaining data protection.
Solution Approach 2:
The system segments the deep learning workflow into distinct isolated environments: a private training environment where models are trained on proprietary data behind firewalls, and a shared evaluation environment where model performance is assessed without exposing data. This segmentation allows the system to achieve both high model accuracy through private training and data privacy protection through environmental isolation.
2Object-affected harmful factors
If proprietary data sets are kept private to maintain data security, then data privacy is protected, but model development and improvement are hindered
Solution Approach 1:
The firewall acts as a mediator that enables collaborative model development across multiple organizations without requiring data sharing. Model architectures, hyperparameters, and performance metrics can be exchanged and evaluated through the firewall interface, maintaining data privacy while enabling productive collaboration and model improvement initiatives.
Solution Approach 2:
The firewall-based evaluation system provides universal functionality that works across different proprietary data sets and organizations. The same evaluation framework can be applied to multiple different data sets without requiring data access, enabling broad model development and comparison while maintaining data security across diverse domains.
3Object-affected harmful factors
If access to training data is restricted for teacher/student frameworks, then data privacy is maintained, but knowledge distillation effectiveness is reduced
Solution Approach 1:
The firewall serves as an intermediary that enables teacher-student knowledge distillation without direct data access. The teacher model processes inputs behind the firewall and provides softened labels or intermediate representations to the student model through the firewall interface. This allows effective knowledge transfer while maintaining data privacy, as the student learns from the teacher's outputs rather than direct access to training data.
Data Source
AI summary
Systems and processes for facilitating the sharing of models trained on a data set confined within a given firewall, i.e., a hidden data set, along with the model's performance metrics are described. The trained models may be used in further processes to improve the trained models to solve a predetermined problem or make a prediction.