On-Device ML Model Sparsification for Resource-Constrained Client Devices
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Solution Overview
Problem
Federated learning frameworks face inefficiencies due to device-specific characteristics variations, leading to sub-optimal performance and resource wastage across client devices, and are limited to training ML models, not effectively addressing testing and feature deployment across diverse devices.
Innovation Solution
Evaluating on-device ML models based on performance measures, activating or sparsifying them to optimize performance and resource usage, and leveraging performance metrics from similar devices to ensure optimal model execution across different hardware and software configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a given instance of an on-device ML model is deployed across diverse client devices, then the model can be shared and managed centrally, but the model performs sub-optimally and wastes computational resources on devices with different characteristics
Solution Approach 1:
The system creates device-specific versions of ML models by sparsifying the model differently for each client device based on its characteristics. Each device receives a customized model instance with specific parameters pruned, ensuring optimal performance for that device's computational capabilities, memory constraints, and latency requirements while avoiding resource waste on less capable devices.
2Adaptability or versatility
If federated learning framework is used for training ML models, then collaborative model improvement across devices is achieved, but the framework is limited and cannot effectively address testing and feature deployment across diverse devices
Solution Approach 1:
The system extends the federated learning framework to perform multiple functions beyond just training. The same framework infrastructure is used for model training, model testing, and feature deployment evaluation across diverse devices. This multi-functional approach allows comprehensive assessment of new features on device-specific hardware before full deployment, improving reliability while maintaining framework versatility.
3Ease of operation
If new features are deployed across all client devices uniformly, then deployment simplicity is maintained, but performance varies sub-optimally across devices with different characteristics
Solution Approach 1:
The system performs preliminary evaluation of new features on a subset of client devices before full deployment. By testing features on diverse devices first and measuring performance metrics, the system identifies device-specific performance issues beforehand. This preliminary action allows optimization of features for different device types before universal deployment, ensuring both deployment simplicity and optimal performance across all devices.
Data Source
AI summary
Implementations disclosed herein are directed to systems and methods for evaluating new feature(s) for client device(s) based on performance measure(s) of the client device(s) and/or the new feature(s). The new feature(s) can include, for example, machine learning (ML) model(s), non-ML software-enabled functionality, non-ML hardware-enabled functionality, and/or ML or non-ML software application features for a given software application utilized by the client device(s). The client device(s) can generate the performance measure(s) by processing a plurality of testing instances for the new feature(s). The performance measure(s) can include, for example, latency measure(s), memory consumption measure(s), CPU usage measure(s), precision and/or recall measure(s), and/or other measures. In some implementations, the new feature(s) may be activated for use locally at the client device(s) based on the performance measure(s), and optionally at other client device(s) that share the same device characteristics. In other implementations, the new feature(s) may be modified based on the performance measure(s).