Dell products l.p. (20240095576). GAN-BASED DATA GENERATION FOR CONTINUOUS CENTRALIZED ML TRAINING simplified abstract
Contents
- 1 GAN-BASED DATA GENERATION FOR CONTINUOUS CENTRALIZED ML TRAINING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 GAN-BASED DATA GENERATION FOR CONTINUOUS CENTRALIZED ML TRAINING - 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
GAN-BASED DATA GENERATION FOR CONTINUOUS CENTRALIZED ML TRAINING
Organization Name
Inventor(s)
Rômulo Teixeira de Abreu Pinho of Niterói (BR)
GAN-BASED DATA GENERATION FOR CONTINUOUS CENTRALIZED ML TRAINING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240095576 titled 'GAN-BASED DATA GENERATION FOR CONTINUOUS CENTRALIZED ML TRAINING
Simplified Explanation
The abstract of the patent application describes a method for training machine learning models using real and/or synthetic data contributed by nodes to a central machine learning service. The models are trained to generate synthetic data based on the distribution of each node, allowing for retraining even when a node is unavailable by incorporating synthetic data from enabled generators.
- Nodes contribute data to a central machine learning service.
- Data is used to train models that can generate synthetic data.
- Models are trained to generate data based on the distribution of each node.
- Synthetic data from enabled generators is used when a node is unavailable for retraining.
Potential Applications
This technology could be applied in various fields such as healthcare, finance, and marketing for improving machine learning model training processes.
Problems Solved
This technology solves the problem of retraining machine learning models even when data contributors are unavailable, ensuring continuous model improvement.
Benefits
The benefits of this technology include increased flexibility in model retraining, improved model accuracy, and the ability to generate synthetic data for training purposes.
Potential Commercial Applications
Potential commercial applications of this technology include data analytics platforms, predictive maintenance systems, and personalized recommendation engines.
Possible Prior Art
One possible prior art for this technology could be the use of synthetic data generation in machine learning model training processes.
Unanswered Questions
How does this technology handle data privacy and security concerns?
This article does not address the specific measures taken to ensure data privacy and security when using real and synthetic data in machine learning model training.
What are the computational requirements for training models with synthetic data?
The article does not provide information on the computational resources needed to train models using synthetic data compared to real data.
Original Abstract Submitted
machine learning model training using real and/or synthetic data is disclosed. nodes contribute data to a central machine learning service. the data is used to train corresponding models whose generators, when trained, are configured to generate synthetic data according to a node's distribution. when a node is unavailable or for other reasons, the data contributed by the node for retraining a machine learning model includes at least some synthetic data from an enabled generator.
