18669247. MODEL TRAINING METHOD AND RELATED APPARATUS simplified abstract (Huawei Technologies Co., Ltd.)
Contents
MODEL TRAINING METHOD AND RELATED APPARATUS
Organization Name
Inventor(s)
Rong Li of Boulogne Billancourt (FR)
MODEL TRAINING METHOD AND RELATED APPARATUS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18669247 titled 'MODEL TRAINING METHOD AND RELATED APPARATUS
Simplified Explanation: This patent application describes a method and apparatus for training neural network models by utilizing parameters from multiple communication devices to improve convergence.
Key Features and Innovation:
- Method involves receiving neural network parameters from one device and sending indication information to another device to participate in training.
- Utilizes correlation coefficient between parameters to determine contribution to model convergence.
- Enables collaboration between devices to enhance training efficiency.
Potential Applications: This technology can be applied in:
- Collaborative machine learning systems
- Federated learning environments
- Multi-device neural network training scenarios
Problems Solved:
- Enhances convergence of neural network models
- Facilitates efficient training across multiple devices
- Improves overall performance of machine learning algorithms
Benefits:
- Faster model convergence
- Enhanced accuracy of neural network models
- Increased efficiency in training processes
Commercial Applications: This technology could be utilized in industries such as:
- Healthcare for collaborative medical diagnostics
- Finance for improved fraud detection systems
- Manufacturing for predictive maintenance in IoT devices
Prior Art: Researchers can explore prior art related to collaborative neural network training methods and federated learning techniques.
Frequently Updated Research: Stay updated on advancements in federated learning, collaborative machine learning, and multi-device neural network training for the latest developments in this field.
Questions about Neural Network Training: 1. How does collaboration between communication devices improve neural network model training? 2. What are the potential challenges of implementing multi-device training methods for neural networks?
Original Abstract Submitted
This application provides a model training method and a related apparatus. In the method, a second communication apparatus receives a first neural network parameter of a first communication apparatus, and sends first indication information to the first communication apparatus when a correlation coefficient between the first neural network parameter and a second neural network parameter of the second communication apparatus is less than a first threshold. The first indication information indicates that the second communication apparatus is to participate in training of a first neural network model of the first communication apparatus. That the correlation coefficient between the first neural network parameter and the second neural network parameter is less than the first threshold indicates that the second neural network parameter makes a great contribution to convergence of the first neural network model.
