Wearable Device Quantization for Privacy and Power
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Solution Overview
Problem
Existing machine learning training methods for wearable devices face challenges in balancing data privacy with the need for effective model training, particularly in determining optimal quantization levels for training models based on device resources and usage patterns.
Innovation Solution
The approach involves determining a quantization level for training machine learning models on wearable devices based on battery power state, usage patterns, and available resources, allowing for local model training without transmitting sensitive user data. This method enables federated learning by aggregating updates from multiple devices while protecting user privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are trained using centralized data collection methods, then model training effectiveness is improved, but data privacy and security are compromised
Solution Approach 1:
The system segments the machine learning training process into distributed local training on wearable devices and centralized aggregation of model updates. Each device trains locally on its own data, avoiding centralized data collection while still enabling effective model training through aggregated updates from multiple devices.
Solution Approach 2:
The system introduces model updates as an intermediary that carries training information without exposing raw user data. Instead of transmitting sensitive data to the server, only the processed model updates are transmitted, acting as a mediator between local devices and the central server to maintain privacy while enabling training effectiveness.
2Measurement precision
If quantization level for model training is increased, then model accuracy is improved, but device battery power consumption increases
Solution Approach 1:
The system dynamically determines the quantization level based on real-time device conditions including battery power state, usage patterns, and available resources. The quantization level is not fixed but adapts to current device state, allowing high accuracy when resources permit and energy efficiency when power is constrained.
Solution Approach 2:
The system changes the quantization parameter based on device conditions. By adjusting this critical parameter dynamically according to battery state and usage patterns, the system optimizes the balance between model accuracy and energy consumption, selecting appropriate quantization levels that adapt to current resource availability.
3Measurement precision
If frequent model training is performed, then model accuracy is improved, but device resources and processing time are consumed
Solution Approach 1:
The system implements periodic model training based on usage patterns and device conditions rather than continuous training. By determining optimal training timing based on accumulated data and resource availability, the system achieves model accuracy improvements while minimizing unnecessary processing resource consumption.
Solution Approach 2:
The device autonomously determines when and how to train models based on its own state including battery level, usage patterns, and data accumulation. This self-service approach eliminates the need for external control of training timing, allowing the device to optimize training frequency based on its actual needs and resource availability.
Data Source
AI summary
Systems, methods, devices, and apparatus are provided for device training at a determined quantization. For instance, a device can include a battery, a communication node, a sensor configured to collect user fitness data, and a processor coupled to the battery, the communication node, and the sensor. The processor can be configured to monitor a power state of the battery, determine a usage pattern of the electronic device and determine a quantization at which to train a machine learning model for analyzing the user fitness data based on the power state and the usage pattern. In addition, the processor can be configured to train the machine learning model at the determined quantization using the user fitness data and transmit the determined quantization and an update to the trained machine learning model via the communication node without transmitting the user fitness data.