Distributed Machine Learning Model Updates Across Sovereign Regions
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Solution Overview
Problem
Data sovereignty regulations restrict the export of user data and personal information, limiting the availability of training data for machine learning models across multiple geographic regions, which hinders the implementation of incremental machine learning techniques in cloud environments.
Innovation Solution
Implementing distributed incremental machine learning techniques that allow machine learning models to be trained and updated within sovereign regions without exporting actual training data, using a coordination server or peer-to-peer communication to facilitate model distribution and updates, ensuring compliance with data export controls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data sovereignty regulations are enforced to keep training data within sovereign regions, then data export control is maintained, but the availability of training data for machine learning models across multiple geographic regions is limited
Solution Approach 1:
The patent extracts the essential learning patterns and knowledge from training data without exporting the actual data itself. Machine learning models are trained locally within each sovereign region using only that region's data, and the trained models (which contain the extracted knowledge) are then shared across regions. This separates the valuable learning information from the restricted training data, allowing model distribution while maintaining data sovereignty compliance.
Solution Approach 2:
The patent introduces machine learning models as intermediaries between training data and predictive analytics. Instead of directly sharing training data between regions, the system uses trained models as intermediaries that encapsulate the learning outcomes. These models act as mediators that transfer predictive capabilities without transferring the restricted training data itself, thus enabling cross-region analytics while maintaining data boundaries.
2Reliability
If machine learning models are trained and updated within sovereign regions without data export, then data sovereignty compliance is maintained, but the predictive power of models across multiple regions is reduced
Solution Approach 1:
The patent creates copies of trained machine learning models and distributes them across multiple sovereign regions. Each region receives a copy of the model that was trained on its local data, enabling the model to make predictions without requiring access to other regions' training data. This copying approach allows predictive capabilities to be replicated and deployed globally while maintaining data sovereignty, as the model copies contain the learned patterns without requiring access to the original training datasets.
Solution Approach 2:
The patent performs preliminary training of machine learning models within each sovereign region before deployment. By pre-training models locally using available data and then distributing these trained models, the system prepares predictive capabilities in advance. This preliminary action ensures that models are ready to provide accurate predictions across regions without requiring real-time access to training data, thus maintaining both compliance and predictive power.
3Productivity
If distributed incremental machine learning techniques are implemented across multiple sovereign regions, then continuous training and updates are enabled, but system complexity increases
Solution Approach 1:
The patent segments the machine learning system into independent regional components, where each sovereign region maintains its own training data and model training processes. This segmentation allows each region to independently train and update models using only its local data, enabling continuous training without requiring centralized data aggregation. The segmented architecture reduces coordination complexity while maintaining the ability to perform incremental updates across regions through model copying and version management.
Data Source
AI summary
The subject disclosure relates to systems for managing the deployment and updating of incremental machine learning models across multiple geographic sovereignties. In some aspects, systems of the subject technology are configured to perform operations including: receiving a first machine learning model via a first coordination agent, the first machine learning model based on a first training data set corresponding with a first sovereign region, sending the first machine learning model to a second coordination agent in a second sovereign region, wherein the second sovereign region is different from the first sovereign region, and receiving a second machine learning model from the second coordination agent, wherein the second machine learning model is based on updates to the first machine learning model using a second training data set corresponding with the second sovereign region. Methods and machine-readable media are also provided.