ML Model Training with Attestation Data Weighting
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Solution Overview
Problem
Edge computing devices face challenges in training machine learning models due to resource constraints and the reliability of sensor data, which can lead to inaccurate predictions and security vulnerabilities, especially in multi-tenant environments where data quality and trustworthiness are compromised.
Innovation Solution
The integration of attestation information objects (AIO) into machine learning models to dynamically assess sensor operational states and apply reliability weighting factors, ensuring that data quality and trustworthiness are considered during both training and inference phases, thereby improving model performance and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensor data is used for training machine learning models in edge computing environments, then model training can be performed with local data, but the reliability and trustworthiness of the training data is compromised due to resource constraints and potential sensor failures
Solution Approach 1:
The system performs preliminary attestation of sensor data sources before using the data for training. Attestation information objects are created and stored in advance, containing verification data that confirms the reliability of sensor readings. This preliminary verification ensures that only trustworthy data is used for model training, resolving the contradiction between utilizing local data for training and ensuring data reliability.
Solution Approach 2:
An intermediary attestation mechanism is introduced between the sensor data and the machine learning training process. The attestation information object serves as a mediator that verifies and validates sensor data before it is used for training. This intermediary layer ensures data trustworthiness while enabling local model training, thus resolving the contradiction between productivity and reliability.
2Device complexity
If all sensor data sources are treated equally during model training, then the training process is simplified, but inaccurate predictions result due to noisy or unreliable data sources
Solution Approach 1:
The system applies different quality levels to different data sources based on their attestation status. Rather than treating all data equally, each sensor or data source is evaluated individually and assigned a weight reflecting its reliability. This local quality approach allows the system to maintain simplicity while improving prediction accuracy by differentiating between trustworthy and noisy data sources.
Solution Approach 2:
The system changes the parameter of data weighting in the training process. Instead of uniform weighting, dynamic weight adjustments are made based on attestation information. This parameter change enables the training process to account for data reliability variations, improving prediction accuracy without significantly increasing complexity through automated weight calculation.
3Measurement precision
If attestation data is integrated into machine learning models to weight data sources, then prediction accuracy improves, but the system complexity increases due to additional verification processes
Solution Approach 1:
Attestation verification is performed in advance and results are stored in attestation information objects. This preliminary action separates the verification process from the training process, allowing the model to use pre-validated data without real-time complexity. The system achieves improved accuracy while managing complexity by performing attestation work beforehand.
Solution Approach 2:
The system uses copies of attestation information rather than re-verifying original sensor data during training. Attestation information objects serve as verified copies that can be efficiently referenced multiple times without repeating the complex verification process. This copying approach maintains prediction accuracy while reducing system complexity during model training operations.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture to train a model using attestation data are disclosed. An example apparatus includes memory, instructions, and at least one processor to execute machine readable instructions to at least access training data originating from an edge device, the training data including telemetry information and attestation information, determine a weighting value to be used for the telemetry information based on the attestation information associated with the edge device, and train a machine learning model based on the telemetry information and the weighting value.