Vehicular Federated Learning With Selective Vehicle Participation
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Solution Overview
Problem
Conventional centralized machine learning approaches for autonomous vehicles face challenges due to privacy concerns related to sensitive sensor data and limited connectivity, leading to inefficiencies in training machine learning models.
Innovation Solution
The implementation of vehicular-network-assisted federated machine learning, where connected vehicles form micro clouds for local model training and horizontal collaboration, sharing metadata and model parameters to enhance training accuracy and reduce communication overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centralized machine learning approach is used to train models by compiling data at a single server, then model training can be performed with complete data access, but privacy-sensitive information about vehicle owners is exposed and large quantities of data must be transmitted over tariffed cellular links
Solution Approach 1:
The system segments the centralized training process into distributed federated learning across multiple vehicles. Each vehicle maintains local data privately while contributing to global model training through iterative parameter updates, eliminating the need to centralize sensitive data.
Solution Approach 2:
A federated learning server acts as an intermediary that coordinates model training without accessing raw sensor data. The server exchanges only model parameters and gradients between vehicles and the central system, preventing direct exposure of privacy-sensitive information while enabling collaborative training.
2Measurement precision
If all vehicles in the network participate in every training phase, then model training comprehensively utilizes available data, but communication overhead and processing load increase significantly
Solution Approach 1:
The system implements partial participation in federated learning by selecting only a subset of vehicles for each training round based on metadata analysis. This reduces communication overhead and processing load while maintaining training effectiveness through strategic selection of participating vehicles.
Solution Approach 2:
The system changes the parameter of participation from binary (all-or-nothing) to selective based on vehicle metadata characteristics. By analyzing sensor capabilities, data quality, and current model performance, the system dynamically determines which vehicles should participate in each training phase.
Data Source
AI summary
Systems and methods for vehicular-network-assisted federated machine learning are disclosed herein. One embodiment transmits first metadata from a connected vehicle to at least one other connected vehicle; receives, at the connected vehicle, second metadata from the at least one other connected vehicle; receives, at the connected vehicle based on analysis of the first and second metadata, a notification that the connected vehicle has been elected to participate in the current training phase of a federated machine learning process; receives, at the connected vehicle, instructions to prepare the connected vehicle for the next training phase; trains a machine learning model to perform a task at the connected vehicle during the current training phase to produce a locally trained machine learning model; and submits the locally trained machine learning model for aggregation with at least one other locally trained machine learning model to produce an aggregated locally trained machine learning model.