Centralized Model Training on Privatized Embeddings for Cross-Domain Knowledge
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Solution Overview
Problem
Knowledge workers face inefficiencies in information sharing and productivity due to specialization in specific knowledge domains, leading to limited access to insights from other domains, misinterpretation of terminology, and lack of context for new knowledge insights, with existing systems requiring extensive training data and resources while failing to protect sensitive information.
Innovation Solution
A collaborative knowledge system that generates a centralized model by training on privatized embeddings from multiple knowledge domains, enabling the sharing of information across diverse areas, protecting sensitive data, and providing context for new insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If knowledge workers specialize in specific knowledge domains, then their expertise and depth in that domain improve, but their ability to share information and access insights from other domains deteriorates
Solution Approach 1:
The system segments knowledge into domain-specific embeddings while maintaining a centralized model that integrates multiple domains. Local models process domain-specific information independently, then contribute to the centralized model, allowing specialists to maintain depth while enabling cross-domain sharing.
Solution Approach 2:
The centralized model acts as an intermediary that receives privatized embeddings from multiple domain-specific local models and synthesizes cross-domain knowledge. This mediator enables knowledge workers to access insights from other domains without directly interacting with those domains' specialized systems.
2Measurement precision
If existing systems use extensive training data to improve model accuracy, then measurement precision improves, but the protection of sensitive information deteriorates
Solution Approach 1:
The system extracts only the necessary semantic patterns from training data into compact embedding vectors, removing sensitive information while preserving knowledge. Local models generate privatized embeddings that capture domain knowledge without exposing raw sensitive documents or data to the centralized system.
Solution Approach 2:
The system transforms raw training data into a different parameter space (embeddings) that preserves semantic information while removing sensitive content. The privatization process changes the representation parameters to protect sensitive information while maintaining model accuracy through the centralized training on these protected embeddings.
Data Source
AI summary
In some implementations, a collaborative knowledge system may receive a first set and a second set of privatized embeddings. The first set of privatized embeddings may be generated by a local model based on a first set of private documents associated with a first knowledge domain. The second set of privatized embeddings may be generated by a local model based on a second set of private documents associated with a second, different knowledge domain. The collaborative knowledge system may train, based on the first and second sets of privatized embeddings, a centralized model. The collaborative knowledge system may receive a query associated with the first knowledge domain or the second knowledge domain. The collaborative knowledge system may generate a response to the query based on processing the query with the centralized model. The collaborative knowledge system may provide the response to the query to a user device.