Vehicular Distributed Learning With Energy-Aware Client Selection
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Solution Overview
Problem
Existing distributed machine learning techniques for vehicular applications do not consider energy consumption, leading to excessive energy use that reduces the driving range of battery-powered electric vehicles and do not effectively balance performance and energy consumption, especially in hybrid distributed machine learning architectures with mobile clients.
Innovation Solution
A method for vehicular distributed machine learning that selects vehicles for training based on model training metrics, including data diversity and energy consumption estimates, allowing local training to optimize energy efficiency and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by stationary object
If distributed machine learning training is performed at vehicular edge devices, then resource consumption at centralized server is reduced, but energy consumption at vehicles increases
Solution Approach 1:
The system dynamically changes the parameter of model training execution location based on real-time conditions. Vehicles can switch between performing training locally (when energy is available) and receiving pre-trained models from the server (when energy is constrained), optimizing the balance between server resource usage and vehicle energy consumption
Solution Approach 2:
The distributed machine learning architecture implements dynamic task allocation where the decision of which vehicle performs training and which receives models is not fixed but adapts based on vehicle energy levels, network conditions, and training progress, allowing the system to respond to changing conditions optimally
2Productivity
If more vehicles perform local training, then model training performance improves, but total energy consumption increases
Solution Approach 1:
Instead of requiring all vehicles to perform full training cycles, the system implements partial training where vehicles contribute training computations proportional to their energy capacity and data quality. Some vehicles perform complete training while others perform lighter validation or feature extraction tasks, achieving good model performance without maximizing total energy consumption
Solution Approach 2:
The system introduces an intermediary coordination layer that manages training task allocation, model aggregation, and performance evaluation. This intermediary optimizes which vehicles participate in training at any given time, ensuring that training performance goals are met while minimizing total energy consumption across the fleet
3Measurement precision
If vehicles with diverse training data are selected for training, then model accuracy improves, but energy consumption varies unpredictably
Solution Approach 1:
The system implements feedback mechanisms where vehicles report their energy consumption, data diversity metrics, and training progress to the coordination layer. Based on this feedback, the system adjusts training task allocation to balance model accuracy requirements with energy consumption constraints, selecting vehicles for training tasks based on real-time conditions rather than static characteristics
Data Source
AI summary
Systems and methods are provided for vehicular distributed machine learning that balances a tradeoff between electrical energy consumed for training a machine learning model and performance of the training. Examples include obtaining model training metrics associated with vehicles, wherein the model training metrics are based on a measure of diversity in training data stored at each respective vehicle and an estimate of energy that the vehicle may consume to train the machine learning model. The examples select a vehicle as a training client based on the obtained first one or more model training metrics; and transmit the machine learning model to the selected vehicle for training by the vehicle.