Multi-Device ML Inference Using Common Feature Space Adaptation
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Solution Overview
Problem
In multi-device environments, devices with varying capabilities and deployment scenarios face challenges in accurately processing input data using a single machine learning model due to differences in data capture quality and ambient noise, leading to inconsistent inference performance.
Innovation Solution
A host device trains a machine learning model using client-device-specific feature extractors to extract features in a common feature space, allowing for accurate inferences across devices by normalizing input data and adjusting models based on device capabilities, and deploying scaled versions to individual devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single machine learning model is used across all devices, then device complexity is reduced, but inference accuracy deteriorates due to varying device capabilities and data capture quality
Solution Approach 1:
The patent applies parameter changes by training multiple versions of the machine learning model with different parameters optimized for specific device capabilities. Each device type receives a model version tuned to its processing power, memory, and data capture characteristics, thereby maintaining high inference accuracy without requiring a single complex model for all devices.
Solution Approach 2:
The patent implements local quality by customizing model parameters locally for each device type rather than using a uniform model. Each device receives a model version with parameters specifically adapted to its local characteristics (processing capability, memory, sensor quality), ensuring optimal performance for each device class while managing complexity.
2Measurement precision
If device-specific machine learning models are trained for each device, then inference accuracy is improved, but computational expense increases
Solution Approach 1:
The patent reduces computational expense by changing model parameters through efficient training techniques. Multiple model versions are trained using parameter-efficient methods such as freezing base model parameters and only training adapter layers, significantly reducing the computational cost compared to training complete models from scratch for each device type.
Solution Approach 2:
The patent segments the model training process into base model components and device-specific adapter components. The base model is trained once on general data, and then lightweight adapter layers are trained separately for each device type, dividing the computational workload and reducing overall training expense while maintaining device-specific optimization.
3Measurement precision
If device-specific models are trained for each device type, then inference accuracy is improved, but device complexity increases
Solution Approach 1:
The patent manages device complexity by systematically varying model parameters based on device capabilities. Rather than creating entirely different model architectures, the approach changes parameters such as model depth, width, and regularization strength to match device constraints, providing device-specific optimization without exponential growth in complexity.
Solution Approach 2:
The patent achieves universality by using a common base model architecture that serves all device types, with device-specific adaptations applied through parameter changes and adapter layers. This multi-functional approach allows a single model framework to serve diverse devices, reducing overall system complexity compared to maintaining completely separate models for each device type.
4Ease of operation
If a common machine learning model is used across all devices, then ease of deployment is improved, but inference performance consistency deteriorates across different devices and environments
Solution Approach 1:
The patent maintains performance consistency by adjusting model parameters for different device types and deployment environments. Each device receives a model version with parameters optimized for its specific characteristics, ensuring reliable and consistent inference performance across diverse hardware and environmental conditions while maintaining a systematic deployment process.
Solution Approach 2:
The patent achieves local quality by customizing model parameters for each device type and deployment scenario. This localized optimization ensures that each device operates at its optimal performance level for its specific conditions, improving overall reliability and consistency across the device fleet while maintaining structured deployment procedures.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for a training and using machine learning models in multi-device network environments. An example computer-implemented method for network communications performed by a host device includes extracting a feature set from a data set associated with a client device using a client-device-specific feature extractor, wherein the feature set comprises a subset of features in a common feature space, training a task-specific model based on the extracted feature set and one or more other feature sets associated with other client devices, wherein the feature sets associated with the other client devices comprise one or more subsets of features in the common feature space, and deploying, to each respective client device of a plurality of client devices, a respective version of the task-specific model.