Edge Node ML Model Adaptation via Centralized Dataset Distillation
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Solution Overview
Problem
Existing methods for deploying machine learning models at edge nodes are costly and not feasible for newly deployed nodes lacking sufficient local data, as they require local domain adaptation which may not be applicable in all cases.
Innovation Solution
A method that involves determining subsets of edge nodes consistently disregarded in robust aggregation, splitting the distillation process for each subset, and using multiple robust distilled datasets to perform domain adaptation of a baseline ML model at a newly deployed edge node.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If local domain adaptation is performed at each edge node, then model accuracy for local conditions is improved, but processing cost and data requirement increase significantly
Solution Approach 1:
The patent introduces a central server as an intermediary that performs dataset distillation and generates synthetic training datasets. This mediator consolidates the complex processing tasks that would otherwise be distributed across all edge nodes, reducing individual node complexity while maintaining model accuracy through centralized sophisticated processing.
Solution Approach 2:
The patent creates synthetic copies of training data through dataset distillation. Instead of requiring each edge node to perform expensive adaptation processes on real local data, the system generates synthetic datasets that capture essential patterns, allowing nodes to train efficiently with these copied representations.
2Adaptability or versatility
If local domain adaptation is performed at each edge node, then model adaptability to local conditions is improved, but feasibility for newly deployed nodes without sufficient data is reduced
Solution Approach 1:
The patent performs dataset distillation and synthetic data generation in advance at the central server before deployment to edge nodes. This preliminary action prepares adaptation-ready datasets that can be immediately used by newly deployed nodes without requiring them to collect sufficient local data first, enabling rapid deployment feasibility.
Solution Approach 2:
The patent transforms real local data into synthetic data representations with modified parameters. By changing the form of data from raw local samples to distilled synthetic datasets, the system maintains adaptability while removing the constraint of requiring large volumes of local data for newly deployed nodes.
3Manufacturing precision
If massive amounts of local data are collected at each edge node, then training quality is improved, but data privacy risks and storage requirements increase
Solution Approach 1:
The patent replaces real local data with synthetic copied data for training purposes. The central server distills essential patterns from aggregated data and creates synthetic representations that maintain training quality while eliminating sensitive information, thus reducing data privacy risks at individual edge nodes.
Solution Approach 2:
The patent extracts only the essential patterns and features from large datasets through distillation, separating the critical training signals from the voluminous raw data. This extraction process retains training quality while removing unnecessary data that would pose privacy risks and storage burdens.
Data Source
AI summary
One example method includes by a central node configured to communicate with edge nodes, determining subsets of the edge nodes, by the central node, splitting a distillation process for each of the subsets of the edge nodes to generate distilled datasets, by the central node, leveraging the distilled datasets to adapt a base machine learning (ML) model for use at a newly deployed edge node that lacks adequate data to adapt the base ML model, and deploying, by the central node, the base ML model, after adaptation, to the newly deployed edge node.