Edge Device Machine Learning Model Update via Query-Based Knowledge Assistance
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Solution Overview
Problem
Edge devices face challenges in efficiently training machine learning models due to limited bandwidth and the inability to locally label out-of-distribution data, requiring efficient communication with remote servers for resource updates.
Innovation Solution
A method for efficient machine learning on edge devices involves determining if captured data is out of distribution, requesting necessary resources from either a second edge device or a server, receiving and updating the inference model with these resources, and making accurate predictions using the updated model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data labeling is performed by querying a centralized server, then prediction accuracy for out-of-distribution data is improved, but bandwidth consumption increases and energy efficiency deteriorates
Solution Approach 1:
The edge device performs preliminary actions by filtering and preprocessing data locally before querying the centralized server. It identifies out-of-distribution data points and prepares only the necessary information for labeling requests, reducing the amount of data transmitted over the network and thus lowering energy consumption while maintaining labeling accuracy.
Solution Approach 2:
The system extracts only the essential information needed for labeling from the captured data. Instead of transmitting the entire dataset to the centralized server, the edge device extracts and sends only the minimal necessary data representations, reducing bandwidth usage and energy consumption while preserving the accuracy of the labeling process.
2Measurement precision
If more data is transmitted to the centralized server for labeling, then model accuracy is improved, but available bandwidth is exhausted and communication efficiency deteriorates
Solution Approach 1:
The edge device extracts only the minimal necessary data representations needed for accurate labeling. It processes and filters the captured data locally to identify only the critical features and characteristics required for out-of-distribution detection, transmitting only these extracted essentials to the centralized server rather than the complete dataset, thus optimizing communication efficiency.
Solution Approach 2:
Preliminary data processing and filtering actions are performed at the edge device before communication with the centralized server. The system pre-identifies out-of-distribution data points and prepares condensed representations of the necessary information, reducing the data volume for transmission while maintaining the information content needed for accurate model training.
3Measurement precision
If continuous model updates are performed on the edge device, then prediction accuracy is improved, but device resources are consumed and computational complexity increases
Solution Approach 1:
The system extracts and transmits only the essential model update parameters and labeling results from the centralized server to the edge device. Instead of receiving and processing complete datasets or full model retraining, the edge device receives condensed update information that can be applied efficiently to improve prediction accuracy for out-of-distribution data without requiring extensive computational resources.
Solution Approach 2:
The system performs partial model updates on the edge device, applying only the necessary changes based on the extracted labeling information from out-of-distribution data. This partial action approach allows the model to improve accuracy for specific challenging data types without requiring full model retraining, thus reducing computational complexity and resource consumption.
Data Source
AI summary
A method, apparatus, and system for efficient machine learning with query-based knowledge assistance includes determining a state of data captured by a sensor in communication with a first edge device to determine if the captured data includes data that is out of distribution based on a trained inference model of the first edge device, if it is identified that an amount of out of distribution data in the captured data is preventing the trained inference model from making an accurate prediction, communicating a request for resources to a second edge device or a server to elicit a response from the second edge device or the server including resources required to update the trained inference model, receiving the requested resources, updating the trained inference model using the received resources, and making a prediction for the received captured data using the updated, trained inference model.