18374812. FEDERATED LEARNING APPARATUS, SERVER APPARATUS, FEDERATED LEARNING SYSTEM, FEDERATED LEARNING METHOD, AND RECORDING MEDIUM simplified abstract (NEC Corporation)
FEDERATED LEARNING APPARATUS, SERVER APPARATUS, FEDERATED LEARNING SYSTEM, FEDERATED LEARNING METHOD, AND RECORDING MEDIUM
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FEDERATED LEARNING APPARATUS, SERVER APPARATUS, FEDERATED LEARNING SYSTEM, FEDERATED LEARNING METHOD, AND RECORDING MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18374812 titled 'FEDERATED LEARNING APPARATUS, SERVER APPARATUS, FEDERATED LEARNING SYSTEM, FEDERATED LEARNING METHOD, AND RECORDING MEDIUM
Simplified Explanation
The abstract describes a federated learning apparatus that trains prediction models to generate information for a receiver. Here is a simplified explanation of the abstract:
- The apparatus trains a prediction model to predict evaluation values for users on evaluation targets.
- It transmits parameter information to a server indicating the prediction model.
- Obtains integrated parameter information from the server, combining information from two prediction models.
- Updates the prediction model using the integrated parameter information.
- Potential Applications of this Technology:
- Personalized recommendations in e-commerce platforms - Customized content delivery in social media platforms
- Problems Solved by this Technology:
- Enhances user experience by providing tailored recommendations - Improves prediction accuracy by incorporating user-specific data
- Benefits of this Technology:
- Increased user engagement and satisfaction - Efficient utilization of data for personalized services
- Potential Commercial Applications of this Technology:
- Enhanced User Experience through Personalized Recommendations
- Potential Commercial Applications of this Technology:
- Possible Prior Art:
- Collaborative filtering algorithms in recommendation systems - Federated learning techniques in machine learning models
- Unanswered Questions:
- How does the apparatus handle privacy concerns when transmitting user data to the server?
- Unanswered Questions:
The abstract does not provide details on the privacy protocols or encryption methods used to protect user data during transmission.
- What is the computational overhead involved in updating the prediction model with integrated parameter information?
The abstract does not mention the computational resources required for updating the prediction model, which could impact the efficiency of the federated learning process.
Original Abstract Submitted
To generate information appropriate for a receiver of the information, a federated learning apparatus includes: a training section which trains a first prediction model that predicts an evaluation value corresponding to a combination of a user and an evaluation target on which the evaluation value is not obtained, using a first training data set including (i) evaluation values of users on evaluation targets and (ii) attribute values of the evaluation targets; a parameter information transmitting section which transmits, to a server apparatus, first parameter information indicating the first prediction model; a parameter information obtaining section which obtains, from the server apparatus, integrated parameter information obtained by integrating the first parameter information and second parameter information indicating a second prediction model trained using a second training data set; and an updating section which updates the first prediction model by replacing the first parameter information with the integrated parameter information.
