Qualcomm incorporated (20240161012). FINE-TUNING OF MACHINE LEARNING MODELS ACROSS MULTIPLE NETWORK DEVICES simplified abstract
Contents
- 1 FINE-TUNING OF MACHINE LEARNING MODELS ACROSS MULTIPLE NETWORK DEVICES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 FINE-TUNING OF MACHINE LEARNING MODELS ACROSS MULTIPLE NETWORK DEVICES - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 How does this technology impact network security?
- 1.11 What are the scalability limitations of this approach?
- 1.12 Original Abstract Submitted
FINE-TUNING OF MACHINE LEARNING MODELS ACROSS MULTIPLE NETWORK DEVICES
Organization Name
Inventor(s)
Hamed Pezeshki of San Diego CA (US)
Taesang Yoo of San Diego CA (US)
Jay Kumar Sundararajan of San Diego CA (US)
FINE-TUNING OF MACHINE LEARNING MODELS ACROSS MULTIPLE NETWORK DEVICES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240161012 titled 'FINE-TUNING OF MACHINE LEARNING MODELS ACROSS MULTIPLE NETWORK DEVICES
Simplified Explanation
The abstract of the patent application describes a method for wireless communications involving transmitting configuration information associated with a trained machine learning model, receiving information associated with a fine-tuned machine learning model, and outputting configuration information associated with the fine-tuned model for transmission to other network devices.
- Explanation of the patent/innovation:
- First network device transmits configuration information related to a trained machine learning model to second network devices.
- Second network devices provide information on a first fine-tuned machine learning model based on adaptation of parameters from the trained model.
- First network device outputs configuration information associated with the fine-tuned model for transmission to third network devices.
Potential Applications
This technology could be applied in various industries such as telecommunications, IoT, and data analytics for improving wireless communication systems.
Problems Solved
This technology helps in optimizing machine learning models for specific network devices, enhancing communication efficiency and performance.
Benefits
The benefits of this technology include improved network communication, enhanced data transmission speeds, and optimized machine learning models for specific network requirements.
Potential Commercial Applications
One potential commercial application of this technology could be in the development of advanced wireless communication systems for smart devices and IoT networks.
Possible Prior Art
One possible prior art could be the use of machine learning models for optimizing wireless communication systems, but the specific method described in the patent application may be novel.
Unanswered Questions
How does this technology impact network security?
This technology focuses on optimizing machine learning models for wireless communication, but it is essential to understand its implications on network security and potential vulnerabilities.
What are the scalability limitations of this approach?
While the method described in the patent application seems promising for wireless communications, it is crucial to explore the scalability limitations when deploying this technology on a larger scale or in complex network environments.
Original Abstract Submitted
an apparatus, method and computer-readable media are disclosed for performing wireless communications. for example, a first network device can transmit, to one or more second network devices, configuration information associated with a trained machine learning model. the first network device can receive, from the one or more second network devices, information associated with a first fine-tuned machine learning model based on adaptation of parameters of the trained machine learning model. the first network device can further output, for transmission to one or more third network devices, configuration information associated with the first fine-tuned machine learning model.
