Federated CV Regression Model for Camera Configuration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing computer vision (CV) systems face challenges in efficiently managing resources and configuring local hardware resource systems for optimal CV inferencing, particularly in handling varying computing resources and data streams across different devices.
Innovation Solution
The implementation of a CV manager that generates a federated CV regression model, which takes configuration inputs to recommend optimal camera configurations for CV inferencing. This involves local hardware resource systems training local CV regression models and merging them to create a federated model, which is then distributed for resource configuration recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CV systems transmit sensitive data to central servers for processing, then centralized model training can be performed, but data security and privacy are compromised
Solution Approach 1:
The patent introduces federated learning as an intermediary mechanism that enables centralized model training without direct data transmission. Local hardware resource systems train models using their own sensitive data locally, then only model parameters or gradients are transmitted to the central server for aggregation, thus achieving centralized training accuracy while protecting data security through the intermediary federated learning protocol
Solution Approach 2:
The patent creates copies of the model parameters and training algorithms that can be executed locally on hardware resource systems. Instead of transmitting original sensitive data to central servers, the system transmits copied model structures and training frameworks that enable distributed training, thereby maintaining data security while achieving centralized coordination benefits
2Productivity
If local hardware resource systems are configured with high computing resources, then CV inferencing performance is improved, but system complexity and cost increase
Solution Approach 1:
The patent implements dynamic resource allocation where the federated learning system automatically adjusts and optimizes hardware resource configurations based on actual CV inferencing workloads and performance requirements. The system can dynamically scale computing resources up or down, and automatically configure optimal hardware settings for different scenarios, thereby improving inferencing performance while reducing the need for permanently over-provisioned complex hardware systems
Solution Approach 2:
The patent utilizes parameter optimization through federated learning to automatically tune hardware resource allocation parameters, processing configurations, and system settings. The system learns optimal parameter values from distributed training across multiple hardware resource systems, enabling high CV inferencing performance with simplified hardware configurations by relying on software-level parameter optimization rather than complex fixed hardware setups
3Speed
If encoded video stream data is processed and stored locally, then data access speed is improved, but storage requirements increase
Solution Approach 1:
The patent extracts and processes only the essential features and metadata from encoded video stream data locally on hardware resource systems, rather than storing complete video streams. The federated learning system extracts relevant training features and model parameters locally, transmitting only these extracted elements to central servers, thereby achieving fast local data access while minimizing storage requirements by storing only processed extracts rather than raw data
Solution Approach 2:
The patent segments video data processing into distributed local operations and centralized coordination. Each hardware resource system independently processes and stores only its local video data segments and extracted features, while the federated learning framework coordinates model training across segmented data sources. This segmentation enables fast local access to segmented data while reducing total storage requirements through distributed rather than centralized storage of complete datasets
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables efficient resource management and configuration recommendations for CV inferencing, ensuring consistent performance across diverse computing devices without requiring sensitive data transmission, thus improving operational efficiency and user experience.
Implementation Method 1
The encoded video stream data and the metadata may be stored in processing system storage via remote direct memory access (RDMA)
Data Source
AI summary
A system in accordance with one or more embodiments comprises a processor, a processing system operatively connected to the processor, and an enhanced networking interface operatively connected to the processing system. The enhanced networking interface is programmed to obtain encoded data from a local data source, perform a decoding of the encoded data to obtain decoded data, provide the decoded data to the processing system. The processing system is programmed to obtain the decoded data from the enhanced networking interface, perform a computer vision (CV) inferencing on the decoded data to obtain inferencing data, provide the inferencing data to the processor, and perform, by the processor, a remediation action based on the inferencing data.