View source for Huawei technologies co., ltd. (20240135191). METHOD, APPARATUS, AND SYSTEM FOR GENERATING NEURAL NETWORK MODEL, DEVICE, MEDIUM, AND PROGRAM PRODUCT simplified abstract

=METHOD, APPARATUS, AND SYSTEM FOR GENERATING NEURAL NETWORK MODEL, DEVICE, MEDIUM, AND PROGRAM PRODUCT=


==Organization Name==

[[:Category:huawei technologies co., ltd.|huawei technologies co., ltd.]]


[[Category:huawei technologies co., ltd.]]

==Inventor(s)==

[[:Category:Mi Luo of Singapore (SG)|Mi Luo of Singapore (SG)]][[Category:Mi Luo of Singapore (SG)]]

[[:Category:Fei Chen of Hong Kong (CN)|Fei Chen of Hong Kong (CN)]][[Category:Fei Chen of Hong Kong (CN)]]

[[:Category:Zhenguo Li of Hong Kong (CN)|Zhenguo Li of Hong Kong (CN)]][[Category:Zhenguo Li of Hong Kong (CN)]]

[[:Category:Jiashi Feng of Singapore (SG)|Jiashi Feng of Singapore (SG)]][[Category:Jiashi Feng of Singapore (SG)]]

==METHOD, APPARATUS, AND SYSTEM FOR GENERATING NEURAL NETWORK MODEL, DEVICE, MEDIUM, AND PROGRAM PRODUCT - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240135191 titled 'METHOD, APPARATUS, AND SYSTEM FOR GENERATING NEURAL NETWORK MODEL, DEVICE, MEDIUM, AND PROGRAM PRODUCT

==Simplified Explanation==

The abstract describes a method, apparatus, and system for generating a neural network model through a federated learning scheme between multiple devices. 

* The first device sends an indication of a subnetwork model structure to a second device, which is determined by adjusting a hypernetwork model structure.
* The second device then calculates a parameter for the subnetwork model based on the indication and the hypernetwork model, which is sent back to the first device.
* The first device trains the subnetwork model using the received parameter and sends the trained model's parameter back to the second device for updating the hypernetwork model.

== Potential Applications ==
This technology can be applied in various fields such as:
* Healthcare for personalized treatment recommendations
* Financial services for fraud detection
* Autonomous vehicles for real-time decision making

== Problems Solved ==
This technology addresses issues such as:
* Efficient model training across multiple devices
* Privacy concerns by keeping data local
* Scalability in large-scale machine learning tasks

== Benefits ==
The benefits of this technology include:
* Improved model accuracy through collaborative learning
* Reduced communication overhead between devices
* Enhanced privacy protection for sensitive data

== Potential Commercial Applications ==
A potential commercial application for this technology could be:
* "Optimizing Federated Learning for Enhanced Data Privacy in Healthcare"

== Possible Prior Art ==
One possible prior art related to this technology is the concept of distributed learning in machine learning, where models are trained across multiple devices without sharing raw data.

=== Unanswered Questions ===

=== How does this technology ensure data privacy during the federated learning process? ===
This technology ensures data privacy by keeping the raw data local to each device and only sharing model parameters during the training process.

=== What is the computational overhead involved in training the subnetwork model on each device? ===
The computational overhead involved in training the subnetwork model on each device depends on the complexity of the model and the amount of data processed during training.


==Original Abstract Submitted==

a method, an apparatus, and a system for generating a neural network model, a device, a medium, and a program product are provided. in an embodiment, a first device sends an indication about a structure of a subnetwork model to a second device, where the subnetwork model is determined by adjusting a structure of a hypernetwork model. the first device receives a parameter of the subnetwork model from the second device, where the parameter of the subnetwork model is determined by the second device based on the indication and the hypernetwork model. the first device trains the subnetwork model based on the received parameter of the subnetwork model. the first device sends a parameter of the trained subnetwork model to the second device for the second device to update the hypernetwork model. in the foregoing manner, an efficient federated learning scheme between a plurality of devices is provided.



[[Category:G06N3/098]]
[[Category:G06N3/082]]