View source for US Patent Application 18004868. DISTRIBUTED MACHINE LEARNING MODEL simplified abstract

=DISTRIBUTED MACHINE LEARNING MODEL=


==Organization Name==

[[:Category:MICROSOFT TECHNOLOGY LICENSING, LLC|MICROSOFT TECHNOLOGY LICENSING, LLC]]


[[Category:MICROSOFT TECHNOLOGY LICENSING, LLC]]

==Inventor(s)==

[[:Category:Yotam Livny of Herzliya (IL)|Yotam Livny of Herzliya (IL)]][[Category:Yotam Livny of Herzliya (IL)]]

[[:Category:Gilad Kirshenboim of Herzliya (IL)|Gilad Kirshenboim of Herzliya (IL)]][[Category:Gilad Kirshenboim of Herzliya (IL)]]

[[:Category:Tal Aviv of Herzliya (IL)|Tal Aviv of Herzliya (IL)]][[Category:Tal Aviv of Herzliya (IL)]]

==DISTRIBUTED MACHINE LEARNING MODEL - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18004868 titled 'DISTRIBUTED MACHINE LEARNING MODEL

==Simplified Explanation==

The patent application describes a method for training machine learning models using distributed computer equipment.
* The method involves obtaining an input data point consisting of a set of values representing different elements of an input feature vector.
* The input data point is then used as input for a first machine learning model on the first computer equipment, which generates at least one output label based on the input data point.
* A partial data point, containing only a subset of the feature vector values, is sent to a second computer equipment.
* The associated label is also sent to the second computer equipment, along with the partial data point.
* This causes the second computer equipment to train a second machine learning model based on the sent partial data and associated label.


==Original Abstract Submitted==

A method comprising, by first computer equipment: obtaining an input data point comprising a set of values, each being a value of a different element of an input feature vector; inputting the input data point to a first machine learning model on the first computer equipment to generate at least one associated output label based on the input data point; sending a partial data point to second computer equipment, the partial data point comprising the values of only part of the feature vector; and sending the associated label to the second computer equipment in association with the partial data point, thereby causing the second computer equipment to train a second machine learning model on the second computer equipment based on the sent part and the associated label.



[[Category:G06N20/00]]