17937225. HORIZONTAL FEDERATED FOREST VIA SECURE AGGREGATION simplified abstract (Dell Products L.P.)
Contents
- 1 HORIZONTAL FEDERATED FOREST VIA SECURE AGGREGATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 HORIZONTAL FEDERATED FOREST VIA SECURE AGGREGATION - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
HORIZONTAL FEDERATED FOREST VIA SECURE AGGREGATION
Organization Name
Inventor(s)
Paulo Abelha Ferreira of Rio de Janeiro (BR)
Adriana Bechara Prado of Niterói (BR)
HORIZONTAL FEDERATED FOREST VIA SECURE AGGREGATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 17937225 titled 'HORIZONTAL FEDERATED FOREST VIA SECURE AGGREGATION
Simplified Explanation
The abstract describes a method for constructing a machine learning model that can screen candidates based on specified characteristics. The method involves broadcasting an indication to start building a random forest model, categorizing features, calculating purity, and determining winning feature splits.
- Broadcasting indication to start building a random forest model
- Categorizing features for decision trees
- Calculating purity based on received information
- Determining winning feature splits
Potential Applications
This technology could be applied in various industries such as recruitment, talent acquisition, and automated screening processes for job applications.
Problems Solved
1. Efficient screening of candidates based on specified characteristics 2. Streamlining the candidate selection process
Benefits
1. Improved accuracy in candidate selection 2. Time-saving in screening processes 3. Scalability for large candidate pools
Potential Commercial Applications
Optimizing recruitment processes using machine learning technology
Possible Prior Art
One possible prior art could be the use of machine learning models for candidate screening in recruitment processes.
Unanswered Questions
How does this method handle data privacy and security concerns?
The article does not address the specific measures taken to ensure data privacy and security when handling candidate information in the screening process.
What is the scalability of this method for large candidate pools?
The article does not provide information on how well this method performs when dealing with a large number of candidates in the screening process.
Original Abstract Submitted
One example method includes constructing a machine learning model which, when completed, is operable to screen candidates, from a group of candidates, to define a candidate pool that has specified characteristics. The constructing includes: broadcasting, from a central node to edges of a federation, an indication that construction of a random forest, of the machine learning model, has started; performing a federated feature categorization, by the central node based on information received from the edges, of a feature to be included in respective decision trees of the edges; based on the categorizing, broadcasting a feature category to the edges; performing, by the central node using respective purity information received from the edges, a federated purity calculation; and based on the federated purity calculation, broadcasting, by the central node to the edges, a winning feature split for the feature.
