Contribution-Aware Federated Learning for Heterogeneous Vehicle Models
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Solution Overview
Problem
Current federated learning frameworks do not account for heterogeneous edge nodes with varying computation resources and hardware elements, which poses challenges in vehicular networks where raw data transmission is constrained by privacy and bandwidth issues, making it difficult to design centralized machine learning models.
Innovation Solution
A contribution-aware federated learning system where vehicles train local machine learning models using local data, obtain metadata about their hardware elements, and transmit these models to a server. The server generates an aggregated model based on the received models and metadata, considering the contribution levels of each vehicle, and updates the models using additional local data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is used to train models distributedly across vehicles, then privacy protection and bandwidth efficiency are improved, but heterogeneous edge nodes with different computation resources and hardware elements cannot be properly accounted for
Solution Approach 1:
The patent changes the parameters of model aggregation by introducing contribution-aware weighting that considers hardware metadata. Instead of uniform aggregation, the system adjusts aggregation parameters based on vehicle computation resources, sensor types, and data quality metrics to properly handle heterogeneous edge nodes while maintaining privacy protection through federated learning
Solution Approach 2:
The patent segments the federated learning process into distinct phases: local model training on individual vehicles, metadata collection about hardware elements, contribution level calculation based on segmented hardware characteristics, and weighted aggregation. This segmentation allows each vehicle to be treated according to its specific capabilities while maintaining overall system coordination
2Measurement precision
If raw data is transmitted to the data center for centralized model training, then model accuracy can be improved, but communication bandwidth requirements and storage constraints are worsened
Solution Approach 1:
The patent extracts only the essential model parameters and metadata from the raw data, transmitting only these extracted elements to the data center instead of the complete raw datasets. This extraction approach maintains model training capability while dramatically reducing communication bandwidth requirements and storage constraints at the centralized server
Solution Approach 2:
The patent creates and transmits copies of trained local models rather than transmitting the original raw data. Each vehicle trains locally on its own data, producing model copies that can be aggregated without requiring the original data to be transmitted, thus achieving centralized model improvement with minimal data transmission
3Ease of manufacture
If uniform model aggregation is used in federated learning, then implementation simplicity is improved, but performance degradation occurs due to heterogeneous vehicle resources
Solution Approach 1:
The patent introduces dynamic weighting in model aggregation where each vehicle's contribution is adjusted based on its hardware capabilities and data quality. The aggregation process becomes dynamic rather than static, with weights automatically calculated based on vehicle-specific metadata including computation resources, sensor types, and operational conditions, thereby improving performance without excessive complexity
Data Source
AI summary
A system for contribution-aware federated learning is provided. The system includes a server and a plurality of vehicles. Each of the plurality of vehicles includes a controller programmed to: train a local machine learning model using first local data; obtain metadata for hardware elements of corresponding vehicle; transmit the trained local machine learning model and the metadata to a server; receive an aggregated machine learning model from the server; and train the aggregated machine learning model using second local data. The server generates the aggregated machine learning model based on the trained local machine learning models and the metadata received from the plurality of vehicles.