Distributed Dataset Distillation for Privacy-Preserving Edge Model Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In edge computing environments, there is a challenge in efficiently storing and managing massive amounts of data for machine learning model training while preserving privacy, especially in resource-constrained near-edge nodes, where data needs to be distilled in a way that maintains privacy and reduces compute costs.
Innovation Solution
Implementing a dataset distillation process that compresses data streams from edge nodes in a distributed manner, using a privacy-preserving algorithm to create a distilled dataset that can pre-train machine learning models, which are then fine-tuned at near-edge nodes, ensuring data privacy and efficiency across multiple organizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If data is stored at edge devices for local processing, then processing resources and bandwidth requirements are reduced, but the ability to centrally train models leveraging data from multiple warehouses is compromised
Solution Approach 1:
The patent extracts only the essential information from the full edge data through distillation techniques, creating a compressed representation that preserves the necessary patterns for centralized model training while removing redundant data that would consume excessive bandwidth and storage resources
Solution Approach 2:
The patent introduces a data distillation layer as an intermediary between edge devices and centralized servers. This intermediary compresses and transforms the raw edge data into a condensed format that maintains predictive power while significantly reducing data volume for transmission and processing
2Ease of operation
If data is distilled to improve efficiency and ease of use, then data management becomes easier, but privacy concerns of entities whose data is distilled may be compromised
Solution Approach 1:
The patent transforms the data representation parameters through distillation, converting raw data into a transformed space where the essential patterns are preserved but the original data structure is fundamentally changed, making it difficult to reverse-engineer sensitive information
Solution Approach 2:
The patent creates a distilled copy of the data that captures the essential patterns and statistical properties needed for model training, while this copy is sufficiently transformed that it does not expose sensitive information about the original data sources
3Quantity of substance
If massive amounts of data are generated by edge devices, then the quality and quantity of training data improve, but storage and processing costs increase significantly
Solution Approach 1:
The patent changes the data parameters through compression and distillation algorithms, reducing the data dimensionality and volume while preserving the essential information needed for training, thereby reducing storage and processing requirements
Solution Approach 2:
The patent discards redundant and less important data components through selective distillation, retaining only the essential patterns and features that contribute most to model training effectiveness, while eliminating excess data that would increase storage and processing costs
Data Source
AI summary
One example method includes, in an environment having a first near-edge node and a second near-edge node, each of which is operable to communicate with a respective set of edge nodes and with a central node: instantiating, by the central node, a dataset distillation process, wherein the dataset includes data collected by the edge nodes, and the data remains at the near-edge nodes and is not accessed by the central node; performing the dataset distillation process to create a distilled dataset; pre-training a machine learning model using the distilled dataset; comparing the pre-trained machine learning model to one or more other pre-trained machine learning models; and deploying, to the edge nodes, the pre-trained learning model that has been determined, based on the comparing, to provide the best performance as among the pre-trained machine learning models that have been compared.