Prediction Model Deployment Using Distributed Data Sketches
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Solution Overview
Problem
Data nodes lack the computing resources and time to perform prediction model training, leading to inefficient data transmission and potential privacy and security vulnerabilities when sharing large datasets for model training, especially in dynamic environments.
Innovation Solution
Deploy prediction models using distributed dataset distillation, where a data node manager generates models using sketches from a subset of registered data nodes, optimizing them with verification data, and updating a registry to manage model deployments efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large datasets are transmitted for model training, then model accuracy can be maintained, but data transmission efficiency deteriorates and privacy/security risks increase
Solution Approach 1:
The patent extracts only the essential information needed for model training by generating sketches that represent subsets of the full dataset. Instead of transmitting complete large datasets, the system extracts distilled sketches containing key patterns and features, thereby maintaining model accuracy while dramatically reducing data transmission requirements and associated energy costs.
Solution Approach 2:
The patent introduces sketches as an intermediary representation between the original dataset and the model training process. These sketches act as a compressed intermediate form that preserves essential training information while eliminating redundant data, thus serving as a mediator that reduces transmission overhead without sacrificing model performance.
2Reliability
If complete datasets are shared for model training, then model performance can be optimized, but privacy and security vulnerabilities increase
Solution Approach 1:
The system extracts only the necessary statistical and pattern information from the complete dataset to create sketches. By taking out only the essential training signals and leaving out sensitive individual data points, the system maintains model performance while eliminating privacy and security vulnerabilities associated with sharing complete datasets.
Solution Approach 2:
Instead of sharing the original complete dataset, the system creates and shares distilled sketch copies that represent the essential characteristics of the data. These sketch copies preserve the training value needed for model performance while being safe to share since they do not contain sensitive individual records or identifiable information.
3Object-affected harmful factors
If data nodes perform model training locally, then data privacy is maintained, but computing resource requirements exceed available capacity
Solution Approach 1:
The patent introduces distilled sketches as an intermediary that enables a hybrid approach: data nodes can perform local training using these compressed sketch representations without needing to process complete datasets. The sketches serve as a lightweight intermediary that preserves privacy benefits while reducing computing resource requirements to feasible levels.
Solution Approach 2:
The system changes the parameter of data representation from complete raw datasets to distilled sketches with reduced dimensionality and complexity. This parameter change transforms the computing task from intractable full-dataset processing to feasible sketch-based training, enabling data nodes with limited resources to participate in model training while maintaining privacy.
4Productivity
If sketches from subsets of data nodes are used for model generation, then deployment efficiency improves, but data representation completeness may be reduced
Solution Approach 1:
The patent merges sketches from multiple data nodes to create a comprehensive distilled representation. By combining information from subset sketches through aggregation and merging operations, the system reconstructs a complete picture that captures diverse data patterns, thereby maintaining data representation completeness while benefiting from the efficiency of using only subset sketches for each contributing node.
Solution Approach 2:
The system performs preliminary distillation actions where each data node pre-processes its data into a sketch before participation in model training. This preliminary action of creating compact sketches in advance enables efficient deployment while the merging of multiple such pre-processed sketches ensures that the collective representation remains complete and comprehensive.
Data Source
AI summary
Techniques described herein relate to a method for managing data nodes. The method includes identifying, by a data node manager associated with a plurality of data nodes, a model deployment event; in response to identifying the model deployment event: generating, by the data node manager, a new model using model generation information from a subset of the plurality of data nodes specified by a data node registry; deploying, by the data node manager, the new model to the plurality of data nodes; and updating the data node registry based on the new model to obtain an updated data node registry.