18063438. SYSTEMS AND METHODS FOR FEDERATED VALIDATION OF MODELS simplified abstract (Capital One Services, LLC)
Contents
- 1 SYSTEMS AND METHODS FOR FEDERATED VALIDATION OF MODELS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS AND METHODS FOR FEDERATED VALIDATION OF MODELS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Federated Machine Learning Models
- 1.13 Original Abstract Submitted
SYSTEMS AND METHODS FOR FEDERATED VALIDATION OF MODELS
Organization Name
Inventor(s)
Jeremy Goodsitt of Champaign IL (US)
Michael Davis of Arlington VA (US)
Taylor Turner of Richmond VA (US)
Tyler Farnan of San Diego CA (US)
SYSTEMS AND METHODS FOR FEDERATED VALIDATION OF MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18063438 titled 'SYSTEMS AND METHODS FOR FEDERATED VALIDATION OF MODELS
Simplified Explanation
This patent application describes methods and systems for improving the accuracy of machine learning models in federated environments by unloading training and validation techniques to client devices using newly collected data.
- Validates machine learning models in federated machine learning model environments
- Unloads training and validation techniques to client devices
- Uses newly collected data to improve accuracy of federated machine learning models
Key Features and Innovation
- Validation of machine learning models in federated environments
- Utilization of client devices for training and validation techniques
- Improvement of model accuracy through newly collected data
Potential Applications
This technology can be applied in various industries such as healthcare, finance, and telecommunications where federated machine learning models are used for data analysis and predictions.
Problems Solved
This technology addresses the challenge of maintaining accuracy in federated machine learning models when training and validation techniques are distributed to client devices.
Benefits
- Enhanced accuracy of machine learning models
- Efficient utilization of client devices for training and validation
- Improved performance in federated machine learning environments
Commercial Applications
- Healthcare: Enhancing predictive models for patient diagnosis and treatment
- Finance: Improving fraud detection algorithms for secure transactions
- Telecommunications: Optimizing network performance through predictive analytics
Prior Art
Readers can explore prior research on federated machine learning models, validation techniques in distributed systems, and data privacy in collaborative learning environments.
Frequently Updated Research
Stay informed about the latest advancements in federated machine learning models, distributed training techniques, and data privacy regulations in collaborative learning settings.
Questions about Federated Machine Learning Models
How does this technology impact data privacy in federated machine learning environments?
This technology enhances data privacy by keeping sensitive information on client devices and only transferring model updates.
What are the potential challenges of implementing federated machine learning models in real-world applications?
Implementing federated machine learning models may face challenges such as network latency, device heterogeneity, and data synchronization.
Original Abstract Submitted
Methods and systems described herein for validating machine learning models in federated machine learning model environments. More specifically, the methods and systems relate to unloading training and validation techniques to client devices using newly collected data to improve accuracy of federated machine learning models.
