View source for 18277172. Machine Learning Model Distribution simplified abstract (Nokia Technologies Oy)

=Machine Learning Model Distribution=


==Organization Name==

[[:Category:Nokia Technologies Oy|Nokia Technologies Oy]]


[[Category:Nokia Technologies Oy]]

==Inventor(s)==

[[:Category:Qiyang Zhao of Antony (FR)|Qiyang Zhao of Antony (FR)]][[Category:Qiyang Zhao of Antony (FR)]]

[[:Category:Stefano Paris of Vanves (FR)|Stefano Paris of Vanves (FR)]][[Category:Stefano Paris of Vanves (FR)]]

[[:Category:Muhammad Majid Butt of Palaiseau (FR)|Muhammad Majid Butt of Palaiseau (FR)]][[Category:Muhammad Majid Butt of Palaiseau (FR)]]

[[:Category:Janne Ali-tolppa of Pirkkala (FI)|Janne Ali-tolppa of Pirkkala (FI)]][[Category: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 ===

=== How does this technology handle privacy and security concerns related to collecting and transmitting real-time data for model optimization? ===
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.



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