Huawei Technologies Co., Ltd. (20240333604). METHOD FOR TRAINING ARTIFICIAL INTELLIGENCE AI MODEL IN WIRELESS NETWORK AND APPARATUS simplified abstract
Contents
- 1 METHOD FOR TRAINING ARTIFICIAL INTELLIGENCE AI MODEL IN WIRELESS NETWORK AND APPARATUS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD FOR TRAINING ARTIFICIAL INTELLIGENCE AI MODEL IN WIRELESS NETWORK AND APPARATUS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Artificial Intelligence Model Training in Wireless Networks
- 1.13 Original Abstract Submitted
METHOD FOR TRAINING ARTIFICIAL INTELLIGENCE AI MODEL IN WIRELESS NETWORK AND APPARATUS
Organization Name
Inventor(s)
Tingting Geng of Shenzhen (CN)
METHOD FOR TRAINING ARTIFICIAL INTELLIGENCE AI MODEL IN WIRELESS NETWORK AND APPARATUS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240333604 titled 'METHOD FOR TRAINING ARTIFICIAL INTELLIGENCE AI MODEL IN WIRELESS NETWORK AND APPARATUS
Simplified Explanation
The patent application describes a method and apparatus for training an artificial intelligence model in a wireless network using federated learning.
- Sending configuration information to terminals participating in federated learning to configure training duration, time-frequency resources, and reporting moments.
- Configuring the same training duration, time-frequency resource, and reporting moment for different terminals.
- Receiving signals obtained through over-the-air superposition of gradients reported by terminals to complete the training of the AI model.
Key Features and Innovation
- Utilizes federated learning in a wireless network for training AI models.
- Configures training parameters for terminals participating in the learning process.
- Utilizes over-the-air superposition of gradients to complete AI model training.
Potential Applications
- Enhancing AI model training efficiency in wireless networks.
- Improving communication and collaboration among terminals in federated learning scenarios.
Problems Solved
- Efficiently training AI models in wireless networks.
- Coordinating training parameters for multiple terminals in federated learning.
Benefits
- Increased efficiency in AI model training.
- Enhanced collaboration and communication among terminals in federated learning.
Commercial Applications
Wireless network providers, AI technology companies, and research institutions can utilize this technology to improve AI model training processes in federated learning scenarios.
Prior Art
Readers can explore prior research on federated learning, wireless network optimization, and AI model training in collaborative environments to understand the background of this technology.
Frequently Updated Research
Stay updated on the latest advancements in federated learning, wireless network optimization, and AI model training to enhance the application of this technology.
Questions about Artificial Intelligence Model Training in Wireless Networks
How does over-the-air superposition of gradients improve AI model training efficiency?
Over-the-air superposition of gradients allows for the completion of AI model training by combining gradients reported by terminals in a wireless network, enhancing the learning process.
What are the key benefits of using federated learning in a wireless network for AI model training?
Federated learning in a wireless network enables efficient collaboration and communication among terminals, leading to improved training outcomes for AI models.
Original Abstract Submitted
a method and apparatus for training an artificial intelligence ai model in a wireless network are provided. the method includes: sending first configuration information to a terminal participating in federated learning, where the first configuration information is used to configure at least one of the following: training duration, a time-frequency resource, and a reporting moment; and same training duration, a same time-frequency resource, and a same reporting moment are configured for different terminals participating in federated learning; and receiving a signal obtained through over-the-air superposition of gradients reported by the terminals, where the gradients are gradients that are of an ai model whose training is completed within the training duration and that are reported by the terminals at the reporting moment by using the time-frequency resource.
