17970298. METHOD AND SYSTEM FOR MANAGING A FEDERATED COMPUTER VISION REGRESSION MODEL USING DISTRIBUTED TRAINING simplified abstract (Dell Products L.P.)

METHOD AND SYSTEM FOR MANAGING A FEDERATED COMPUTER VISION REGRESSION MODEL USING DISTRIBUTED TRAINING

Organization Name

Dell Products L.P.

Inventor(s)

Ian Roche of Glanmire (IE)

Philip Hummel of San Jose CA (US)

Dharmesh M. Patel of Round Rock TX (US)

METHOD AND SYSTEM FOR MANAGING A FEDERATED COMPUTER VISION REGRESSION MODEL USING DISTRIBUTED TRAINING - A simplified explanation of the abstract

This abstract first appeared for US patent application 17970298 titled 'METHOD AND SYSTEM FOR MANAGING A FEDERATED COMPUTER VISION REGRESSION MODEL USING DISTRIBUTED TRAINING

Simplified Explanation

The abstract describes a method for managing hardware resources using computer vision technology. The method involves training a federated computer vision regression model by combining local models from two hardware systems, each with a camera system and processing system.

• The method involves obtaining a request for a federated computer vision regression model.
• Initial training of the federated model is done using an initial training dataset.
• Training requests are sent to two local hardware systems to train local computer vision regression models.
• The federated model is generated by combining the two local models.
• The federated model is distributed back to the local hardware systems.

Potential Applications

This technology could be applied in various industries such as surveillance, autonomous vehicles, robotics, and healthcare for tasks like object detection, tracking, and classification.

Problems Solved

This method helps in efficiently managing hardware resources for computer vision tasks by distributing the workload among multiple local systems.

Benefits

The method allows for collaborative training of computer vision models, enabling better performance and accuracy by leveraging resources from multiple hardware systems.

Potential Commercial Applications

"Optimizing Hardware Resources for Federated Computer Vision Regression Models"

Possible Prior Art

There may be prior art related to federated learning techniques in machine learning and computer vision, as well as research on distributed training of neural networks.

How does this method handle data privacy and security concerns in a federated learning setup?

The abstract does not provide details on how data privacy and security are addressed when training the federated computer vision regression model using data from multiple local hardware systems.

What are the specific hardware requirements for implementing this method effectively?

The abstract does not mention the specific hardware requirements needed for training and deploying the federated computer vision regression model across multiple local systems.

Original Abstract Submitted

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.