Modular Knowledge Sharing for Privacy-Preserving Vehicle Fault Detection
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Solution Overview
Problem
Existing vehicle fault detection systems face challenges in transferring knowledge and training personalized models for new entities with limited historical data, while ensuring data privacy and addressing the divergence among entities.
Innovation Solution
A Modular Network based Knowledge Sharing Model (MNKS) employs an entity-shared modular and entity-specific decoder, utilizing reinforcement learning for network routing and edge computing to extract common knowledge and personalize models for different entities, ensuring data privacy by processing data on the edge-side.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional machine learning methods train a unified model for all entities, then model training efficiency is improved, but the model cannot capture entity-specific features and attributes
Solution Approach 1:
The patent divides the model into modular components including entity-shared modules that capture common knowledge and entity-specific modules that capture individual characteristics. This segmentation allows the system to efficiently share knowledge across entities while maintaining the ability to adapt to entity-specific features, resolving the contradiction between training efficiency and adaptability.
Solution Approach 2:
The patent implements dynamic module selection and assembly mechanisms that adaptively configure which shared modules and which entity-specific modules are activated for each entity. This dynamic approach allows the system to optimize performance for each entity while maintaining overall system efficiency through knowledge sharing.
2Adaptability or versatility
If personalized models are trained for each entity, then entity-specific features are captured, but data insufficiency emerges especially for emerging entities lacking historical data
Solution Approach 1:
The patent pre-trains entity-shared modules using aggregated data from multiple entities before deploying them to individual entities. This preliminary action allows emerging entities with limited data to immediately benefit from knowledge learned from the broader population, enabling personalized modeling without requiring extensive entity-specific historical data.
Solution Approach 2:
The entity-shared modules act as intermediaries that transfer knowledge from the population level to the individual entity level. These shared modules capture common patterns and knowledge that can be transferred to entities with limited data, serving as a bridge that enables personalized modeling even when entity-specific training data is insufficient.
3Measurement precision
If complete training data is collected for model training, then model accuracy is improved, but data collection time increases to 1-2 months
Solution Approach 1:
The patent creates entity-shared modules that serve multiple entities simultaneously, capturing universal knowledge and patterns that apply across the population. This multi-functionality allows the system to achieve high prediction accuracy for individual entities without requiring each entity to accumulate extensive its own training data, as the shared modules provide transferable knowledge.
4Productivity
If data is centralized for processing, then model training efficiency is improved, but data privacy security is compromised
Solution Approach 1:
The patent segments the data processing architecture into edge devices that handle local entity-specific data and cloud servers that handle entity-shared module training. This segmentation allows efficient centralized processing for shared knowledge while keeping sensitive entity-specific data localized, thereby maintaining data privacy security while achieving processing efficiency.
Solution Approach 2:
The entity-shared modules serve as intermediaries that enable knowledge transfer without requiring direct access to raw entity-specific data. The modules capture essential patterns and knowledge while preserving data privacy, acting as a mediator that achieves efficient knowledge sharing without compromising security.
Data Source
AI summary
A method for vehicle fault detection is provided. The method includes training, by a cloud module controlled by a processor device, an entity-shared modular and a shared modular connection controller. The entity-shared modular stores common knowledge for a transfer scope, and is formed from a set of sub-networks which are dynamically assembled for different target entities of a vehicle by the shared modular connection controller. The method further includes training, by an edge module controlled by another processor device, an entity-specific decoder and an entity-specific connection controller. The entity-specific decoder is for filtering entity-specific information from the common knowledge in the entity-shared modular by dynamically assembling the set of sub-networks in a manner decided by the entity specific connection controller.