18063414. METHODS AND SYSTEMS FOR UTILIZING DATA PROFILES FOR CLIENT CLUSTERING AND SELECTION IN FEDERATED LEARNING simplified abstract (Capital One Services, LLC)

METHODS AND SYSTEMS FOR UTILIZING DATA PROFILES FOR CLIENT CLUSTERING AND SELECTION IN FEDERATED LEARNING

Organization Name

Capital One Services, LLC

Inventor(s)

Tyler Farnan of San Diego CA (US)

Jeremy Goodsitt of Champaign IL (US)

Michael Davis of Arlington VA (US)

Taylor Turner of Richmond VA (US)

Kenny Bean of Herndon VA (US)

METHODS AND SYSTEMS FOR UTILIZING DATA PROFILES FOR CLIENT CLUSTERING AND SELECTION IN FEDERATED LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 18063414 titled 'METHODS AND SYSTEMS FOR UTILIZING DATA PROFILES FOR CLIENT CLUSTERING AND SELECTION IN FEDERATED LEARNING

Simplified Explanation: This patent application describes methods and systems for enhancing federated learning by improving its applicability, increasing training efficiency, ensuring comprehensive training data availability, and enhancing the rate of training a global model.

• Key Features and Innovation:

   - Improving the applicability of federated learning across various applications
   - Increasing the efficiency of training a global model through federated learning
   - Ensuring comprehensive training data availability to models assigned by the federated learning server
   - Improving the rate of training a global model through federated learning

• Potential Applications:

   - Machine learning systems
   - Data analytics platforms
   - IoT devices
   - Healthcare data management

• Problems Solved:

   - Limited applicability of federated learning
   - Inefficient training of global models
   - Inadequate training data availability
   - Slow training rates

• Benefits:

   - Enhanced performance of machine learning models
   - Improved data privacy and security
   - Increased efficiency in training processes
   - Better utilization of distributed data sources

• Commercial Applications:

   - Enhanced predictive analytics in various industries
   - Improved personalized recommendations in e-commerce
   - Secure and efficient data processing in healthcare

• Prior Art:

   - Researchers have explored various techniques to improve federated learning efficiency and data availability, such as differential privacy mechanisms and data aggregation strategies.

• Frequently Updated Research:

   - Ongoing research focuses on optimizing communication efficiency in federated learning systems and developing robust federated learning algorithms.

Questions about Federated Learning: 1. How does federated learning differ from traditional centralized machine learning approaches? 2. What are the key challenges in implementing federated learning across diverse applications?

Original Abstract Submitted

Methods and systems are described for novel uses and/or improvements to federated learning. As one example, methods and systems are described for improving the applicability of federated learning across various applications and increasing the efficiency of training a global model through federated learning. As another example, methods and systems are described for ensuring comprehensive training data is available to models assigned by the federated learning server. Additionally, methods and systems are described for improving the rate of training a global model through federated learning.