Edge Device Model Distillation for Privacy and Bandwidth
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Solution Overview
Problem
Existing methods for training and evaluating machine-learned models on mobile devices are hindered by privacy concerns and high bandwidth data streams, making it infeasible to stream all user data centrally, and often result in delayed model training due to insufficient data history.
Innovation Solution
A method where a user computing device executes a teacher machine-learned model to produce output data, evaluates characteristics such as performance and storage usage, and trains a student machine-learned model to replace the teacher model, optimizing for smaller size and faster runtime, using locally logged user-specific data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If all user data is logged centrally to train machine-learned models, then model training data availability is improved, but privacy risk and bandwidth consumption increase
Solution Approach 1:
The patent extracts only the essential training signals needed for model improvement and leaves sensitive user data on local devices. Student models are trained locally using distilled knowledge from teacher models, eliminating the need to centralize raw user data while still enabling model training.
Solution Approach 2:
The patent introduces teacher-student model distillation as an intermediary mechanism. Instead of directly using raw user data for training, the system uses teacher models to generate distilled training signals that convey essential patterns without exposing sensitive information, acting as a mediator between data privacy and model training needs.
2Quantity of substance
If all user data is streamed centrally, then model training completeness is improved, but bandwidth requirements become infeasible
Solution Approach 1:
The patent extracts only the essential training patterns and relationships from user data through teacher model processing, then transmits only these distilled signals to student models. This extraction approach captures the essential training information while dramatically reducing the volume of data that needs to be transmitted.
Solution Approach 2:
The patent creates simplified copies of the training knowledge through model distillation. Student models receive compressed representations of training patterns in the form of distilled signals from teacher models, rather than receiving complete raw datasets, enabling training with minimal bandwidth consumption.
3Object-affected harmful factors
If data is logged prudently to minimize privacy risks, then privacy protection is improved, but model training data availability worsens
Solution Approach 1:
The patent enables student models to perform self-service training locally using distilled signals from teacher models. This eliminates the need for extensive central data logging, as each device can independently train its student model using locally processed training signals, maintaining privacy while ensuring data availability.
Solution Approach 2:
The patent transforms the training data from raw user data into distilled training signals through the teacher-student model framework. This parameter transformation changes the form of the data from sensitive raw inputs to abstracted training patterns, maintaining training effectiveness while reducing privacy risks.
4Measurement precision
If teacher machine-learned models are used to produce output data, then model accuracy is improved, but device computational resources are consumed
Solution Approach 1:
The patent extracts the essential computational knowledge from resource-intensive teacher models and transfers it to lightweight student models. Student models perform the actual inference tasks with minimal computational resources, while teacher models are only used periodically for distilling new training signals, reducing overall resource consumption.
Solution Approach 2:
The patent creates simplified student model copies that replicate the essential functionality of teacher models with much lower computational requirements. These student models handle daily inference operations, freeing device resources while maintaining acceptable accuracy through the distillation process.
Data Source
AI summary
The present disclosure provides systems and methods for evaluating and selectively distilling machine-learned models on edge devices. A method can include executing, by a user computing device of a computing system, a teacher machine-learned model stored by the user computing device to produce output data from input data; evaluating, by the computing system, a characteristic of one or more of the user computing device and the teacher machine-learned model; determining, by the computing system based on the evaluation, to train a student machine-learned model that is stored by the user computing device; and training, by the user computing device, the student machine-learned model based on the teacher machine-learned model.