18159246. SYSTEM AND METHOD FOR MANAGEMENT OF DISTRIBUTED INFERENCE MODEL GENERATION simplified abstract (Dell Products L.P.)
Contents
SYSTEM AND METHOD FOR MANAGEMENT OF DISTRIBUTED INFERENCE MODEL GENERATION
Organization Name
Inventor(s)
PHILIP E. Hummel of San Jose CA (US)
DHARMESH M. Patel of Round Rock TX (US)
SYSTEM AND METHOD FOR MANAGEMENT OF DISTRIBUTED INFERENCE MODEL GENERATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18159246 titled 'SYSTEM AND METHOD FOR MANAGEMENT OF DISTRIBUTED INFERENCE MODEL GENERATION
Simplified Explanation: The patent application describes methods and systems for providing computer implemented services using inference models obtained through federated learning.
- Inference models are generated using siloed data with distribution restrictions.
- Selected instances of the inference models are used for continued learning to obtain a final inference model.
- The final inference model is used to generate output for the computer implemented services.
Key Features and Innovation:
- Utilization of federated learning to obtain inference models.
- Generation of inference models using siloed data with distribution restrictions.
- Selection of instances of inference models for continued learning to obtain a final model.
Potential Applications: The technology can be applied in various industries such as healthcare, finance, and marketing for personalized services and recommendations.
Problems Solved: The technology addresses the challenges of utilizing siloed data with distribution restrictions to generate inference models for computer implemented services.
Benefits:
- Improved accuracy and efficiency in generating inference models.
- Enhanced personalization and customization of computer implemented services.
- Increased privacy and security of data through federated learning.
Commercial Applications: The technology can be used in industries such as e-commerce, healthcare, and finance for personalized recommendations, predictive analytics, and targeted marketing strategies.
Questions about Federated Learning: 1. How does federated learning ensure data privacy and security? 2. What are the potential challenges of implementing federated learning in real-world applications?
Frequently Updated Research: Stay updated on the latest advancements in federated learning and its applications in various industries to leverage the technology effectively.
Original Abstract Submitted
Methods and systems for providing computer implemented services using inference models are disclosed. The inference models may be obtained through federated learning, and may be used to generate output used in the computer implemented services. During the federated learning, instances of inference models may be generated using siloed data with distribution restrictions. Some of the instances of the inference models may be selected for continued learning to obtain a final inference model used to generate the output.
