18277172. Machine Learning Model Distribution simplified abstract (Nokia Technologies Oy)
Contents
- 1 Machine Learning Model Distribution
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Machine Learning Model Distribution - 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 Original Abstract Submitted
Machine Learning Model Distribution
Organization Name
Inventor(s)
Muhammad Majid Butt of Palaiseau (FR)
Janne Ali-tolppa of Pirkkala (FI)
Machine Learning Model Distribution - A simplified explanation of the abstract
This abstract first appeared for US patent application 18277172 titled 'Machine Learning Model Distribution
Simplified Explanation
The patent application describes a system where a client device receives a validation model from a centralised unit device, collects radio measurements, obtains predicted parameters, compares collected and predicted parameters, computes gradient vectors, and transmits them back to the centralised unit device.
- Client device receives validation model from centralised unit device
- Collects radio measurements and parameters
- Obtains predicted parameters from validation model
- Compares collected and predicted parameters
- Computes gradient vectors for model parameters
- Transmits gradient vectors back to centralised unit device
Potential Applications
This technology could be applied in various fields such as telecommunications, IoT, and data analytics for optimizing machine learning models based on real-time data.
Problems Solved
This technology helps in improving the accuracy and efficiency of machine learning models by continuously updating and optimizing model parameters based on real-world data.
Benefits
The benefits of this technology include enhanced predictive capabilities, improved performance of machine learning models, and better adaptation to changing environments.
Potential Commercial Applications
One potential commercial application of this technology could be in the telecommunications industry for optimizing network performance and predicting user behavior based on real-time data.
Possible Prior Art
One possible prior art for this technology could be similar systems used in the field of machine learning and data analytics for model optimization and parameter tuning.
Unanswered Questions
This article does not address the specific methods or protocols used to ensure the privacy and security of the collected data during the process of model optimization.
What are the potential limitations or challenges of implementing this technology in real-world applications?
This article does not discuss the potential obstacles or difficulties that may arise when implementing this technology in practical settings, such as scalability issues or compatibility with existing systems.
Original Abstract Submitted
According to an example embodiment, a client device is configured to receive a validation model from a centralised unit device, wherein the validation model includes a machine learning model configured to predict an output from an input based on a plurality of model parameters; collect radio measurements corresponding to the input of the validation model and parameters corresponding to the output of the validation model; obtain predicted parameters as the output of the validation model by feeding the collected radio measurements as the input into the validation model; compare the collected parameters and the predicted parameters; compute a plurality of gradient vectors for the plurality of model parameters of the validation model based on the comparison between the collected parameters and the predicted parameters; and transmit the plurality of gradient vectors for the plurality of model parameters of the validation model to the centralised unit device.
