Federated CV Regression Model for Cross-Device Resource Consistency
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Solution Overview
Problem
Existing computing systems face challenges in providing consistent computer vision services across devices with varying computing resources without compromising user experience.
Innovation Solution
A federated computer vision regression model is generated using distributed training, which involves local hardware resource systems to create local models that are merged and distributed for efficient resource configuration recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a centralized computer vision model is deployed across devices with varying computing resources, then service consistency can be maintained, but device complexity and resource requirements increase
Solution Approach 1:
The patent divides the computer vision model into two components: a centralized regression model that determines hardware configuration recommendations, and local hardware resource systems that execute the actual computer vision tasks. This segmentation allows the complex model logic to be separated from the device deployment, reducing on-device complexity while maintaining service consistency through centralized coordination.
Solution Approach 2:
The patent introduces a CV manager as an intermediary component that receives hardware configurations from local systems, generates regression models to determine recommended configurations, and communicates with local hardware resource systems. This intermediary layer abstracts the complexity of model management from individual devices, enabling consistent service delivery without increasing device-level complexity.
2Productivity
If hardware configurations are optimized for each device, then resource efficiency improves, but system complexity increases
Solution Approach 1:
The patent uses a regression model that takes hardware configuration parameters as input and outputs optimized recommendations. The model dynamically adjusts configuration parameters based on device capabilities and task requirements, achieving resource efficiency through parameter optimization rather than complex configuration management logic on individual devices.
Solution Approach 2:
The local hardware resource systems autonomously provide their own hardware configuration information to the CV manager, which then generates optimized recommendations. The system performs self-assessment and self-optimization without requiring complex manual configuration, improving resource efficiency while keeping system complexity low at the device level.
3Measurement precision
If distributed training is used to create federated models, then model accuracy improves, but training time and computational resources increase
Solution Approach 1:
The patent segments the training process into two phases: an initial centralized training phase where the regression model is trained on aggregated data, and subsequent local execution phases where pre-trained models are deployed. This segmentation enables model accuracy improvement through distributed training while reducing overall training time by avoiding continuous centralized training and leveraging pre-trained models for deployment.
Data Source
AI summary
A method for managing hardware resources comprises obtaining, by a computer vision (CV) manager, a request for a federated CV regression model, in response to the request: performing an initial training of the federated CV regression model using an initial training dataset to obtain an initial federated CV regression model, sending training requests to two local hardware resource systems, wherein each local hardware resource system implements a local camera system and a processing system, and wherein the training request comprises training a local CV regression model based on the processing system and the local camera system, obtaining the first local CV regression model and the second local CV regression model, generating the federated CV regression model using the two local CV regression models, and distributing the federated CV regression model to the first local hardware resource system and the second local hardware resource system.