MODEL TRAINING METHOD AND RELATED APPARATUS

Organization Name

huawei technologies co., ltd.

Inventor(s)

Yunfei Qiao of Hangzhou (CN)

Rong Li of Boulogne Billancourt (FR)

Jian Wang of Hangzhou (CN)

MODEL TRAINING METHOD AND RELATED APPARATUS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240311648 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 indicating to another device to participate in training if its parameters contribute significantly to convergence.
Utilizes correlation coefficients between neural network parameters to determine the contribution of each device to model training.

Potential Applications

This technology can be applied in various fields such as telecommunications, artificial intelligence, and data analytics where collaborative training of neural networks is required.

Problems Solved

Enhances the convergence of neural network models by leveraging the collective knowledge of multiple devices.
Improves the efficiency and accuracy of model training by involving multiple communication devices in the process.

Benefits

Faster convergence of neural network models.
Enhanced accuracy and performance of trained models.
Increased efficiency in training processes by utilizing distributed computing resources.

Commercial Applications

This technology can be utilized in industries such as telecommunications, IoT, and cloud computing for optimizing neural network training processes and improving model performance.

Prior Art

Further research can be conducted in the field of distributed neural network training methods to explore similar approaches and advancements in collaborative model training.

Frequently Updated Research

Stay updated on the latest developments in distributed neural network training methods and collaborative model training techniques to enhance the efficiency and accuracy of neural network models.

Questions about Neural Network Model Training

How does this method improve the convergence of neural network models?

This method improves convergence by leveraging the contributions of multiple communication devices in the training process, enhancing the overall accuracy and efficiency of the models.

What are the potential applications of collaborative neural network training in different industries?

Collaborative neural network training can be applied in various industries such as telecommunications, artificial intelligence, and data analytics to improve model performance and accuracy.

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.