Transfer Learning Without Local Data Export in Multi-Node ML
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Solution Overview
Problem
Current machine learning technologies face challenges in training domain-specific models without compromising local data privacy, as they require exporting data to a central training system, which can be costly, latency-prone, and insecure, especially when dealing with multiple domains and evolving data characteristics.
Innovation Solution
The method involves distributing a trained base model to nodes, where local transfer learning occurs without data export, using meta-metrics to cluster nodes with similar characteristics, and iteratively updating model parameters to create domain-specific models within clusters, maintaining data privacy and leveraging local data from multiple nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If local data is exported to a central training system for model training, then model training can be performed with access to diverse data, but data privacy is compromised and system latency increases
Solution Approach 1:
The patent introduces meta-metrics as an intermediary representation that captures essential data characteristics without exposing actual data. Nodes export meta-metrics instead of raw data, allowing the central system to understand data distribution and train accurate models while preserving data privacy. This intermediary layer enables communication of data properties without data leakage.
Solution Approach 2:
The patent extracts only the necessary statistical properties (meta-metrics) from local data while leaving the actual data at the node. By taking out only the essential characteristics needed for model training rather than the complete data set, the system achieves training accuracy while maintaining data privacy and reducing export costs.
2Adaptability or versatility
If local data is exported to a central training system, then domain-specific models can be trained, but system cost and latency increase
Solution Approach 1:
The patent extracts only meta-metrics (statistical properties) from local data instead of exporting the complete data set. This selective extraction dramatically reduces the volume of data that needs to be transmitted, thereby reducing export time and latency while still providing sufficient information for domain-specific model training.
Solution Approach 2:
The patent uses lightweight meta-metrics as disposable representations of data characteristics. These meta-metrics are inexpensive to compute and transmit compared to actual data, enabling frequent updates and iterations without significant time or resource costs.
3Extent of automation
If local data is exported to a central training system, then centralized model training can be performed, but security risks increase
Solution Approach 1:
The patent introduces meta-metrics as a secure intermediary that enables centralized model training without exposing sensitive data. The central system receives and processes only aggregated statistical properties rather than individual data points, maintaining automation benefits while eliminating security vulnerabilities associated with data exposure.
4Measurement precision
If nodes are clustered based on domain characteristics, then transfer learning accuracy improves, but determining domains requires local data analysis
Solution Approach 1:
The patent uses meta-metrics as an intermediary for domain classification. Instead of analyzing actual local data to determine domains, the system analyzes meta-metrics that represent data characteristics. This enables accurate domain identification and clustering without exposing or processing sensitive local data.
Data Source
AI summary
A trained base model is distributed to a set of nodes. From a first node in the set of nodes, a first set of meta-metrics resulting from a transfer learning operation on the trained base model at the first node is collected. The transfer learning at the first node uses first local data available at the first node. The first node is clustered in a cluster with a second node from the set of nodes, in response to a meta-metric in the first set of meta-metrics being within a tolerance value of a corresponding meta-metric in a second set of meta-metrics collected from the second node. A normalized set of model parameters is constructed after an iteration of transfer learning or local learning at the first and second nodes. The normalized set of model parameters is distributed to the first node and the second node in the cluster.