Vehicle-Edge Association for Heterogeneous Federated Learning
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Solution Overview
Problem
Hierarchical federated learning networks face challenges in reducing data heterogeneity due to vehicles generating data under non-identically distributed conditions, leading to models that may not perform well across different scenarios, and existing association methods incur high time and space complexity or only consider data heterogeneity without accounting for system heterogeneity.
Innovation Solution
A vehicle-to-edge server association scheme that considers vehicular system heterogeneity, such as computational resources and privacy requirements, to select the optimal edge server for each vehicle, allowing for the identification and use of characteristic-centric models that minimize data heterogeneity by associating vehicles with edge servers hosting suitable models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If vehicles are associated with edge servers without considering system heterogeneity, then the association process is simple, but data heterogeneity increases and model performance deteriorates
Solution Approach 1:
The patent introduces system heterogeneity parameters (computational resources, privacy requirements, communication capabilities) to characterize vehicles and matches them with corresponding edge servers. This parameter-based matching reduces data heterogeneity by ensuring vehicles with similar characteristics are grouped together, improving model performance without excessive complexity
Solution Approach 2:
The patent creates heterogeneous edge servers with specialized capabilities tailored to different vehicle types. Each edge server is optimized to handle specific vehicle characteristics (e.g., autonomous vehicles, connected vehicles), allowing for more effective local model training while maintaining overall system performance
2Ease of manufacture
If existing association methods are used, then the implementation is straightforward, but time and space complexity increase
Solution Approach 1:
The patent performs preliminary characterization of vehicles based on system heterogeneity parameters before the actual association process. By pre-categorizing vehicles according to their computational resources, privacy requirements, and communication capabilities, the system reduces the time complexity of the matching process while maintaining implementation simplicity
Solution Approach 2:
The patent creates simplified representations (profiles) of vehicles based on their system heterogeneity characteristics. These profiles serve as copies that capture essential attributes without requiring full vehicle data, enabling efficient matching and reducing both time and space complexity
3Adaptability or versatility
If data from all vehicles is aggregated for model training, then comprehensive model coverage is achieved, but privacy requirements are violated
Solution Approach 1:
The patent segments the federated learning network into multiple edge servers, each handling specific types of vehicles based on their characteristics. This segmentation allows comprehensive model coverage within each segment while maintaining privacy by keeping data processing distributed and localized, preventing central aggregation of sensitive information
Solution Approach 2:
The patent introduces edge servers as intermediaries between vehicles and the central cloud server. These intermediaries perform local model training and aggregation, enabling comprehensive learning from diverse vehicle data while protecting privacy by preventing direct access to raw vehicle data at the central server
4Ease of operation
If computational resources are allocated without considering vehicle heterogeneity, then resource distribution is uniform and simple, but computational efficiency decreases
Solution Approach 1:
The patent uses system heterogeneity parameters to dynamically allocate computational resources to vehicles and edge servers. By considering computational capabilities, communication bandwidth, and privacy requirements as parameters, the system optimizes resource distribution to match actual needs, improving computational efficiency while maintaining operational simplicity through automated parameter-based allocation
Data Source
AI summary
Systems and methods are provided for vehicular assisted hierarchical federated learning using a vehicle-to-edge server association scheme that reduces statistical heterogeneity in hierarchical federated learning networks. According to some embodiments, the methods and systems comprise obtaining system conditions of a vehicle based on joining a hierarchical federated learning network, exchanging data between the vehicle and edge servers of the hierarchical federated learning network according to a vehicle-to-edge server association protocol that is based on the vehicular system conditions, and identifying a model for the vehicle from models hosted on the edge servers. Embodiments disclosed herein also include at least one of: training the identified model at the vehicle to produce a locally trained model, and applying data acquired by the vehicle to the identified model to perform a task.