Nvidia corporation (20240303504). FEDERATED LEARNING TECHNIQUE simplified abstract
Contents
FEDERATED LEARNING TECHNIQUE
Organization Name
Inventor(s)
Holger Reinhard Roth of Rockville MD (US)
Dong Yang of Pocatello ID (US)
Vishwesh Nath of Nashville TN (US)
FEDERATED LEARNING TECHNIQUE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240303504 titled 'FEDERATED LEARNING TECHNIQUE
Simplified Explanation: The patent application describes apparatuses, systems, and techniques for training and using neural networks, with a focus on aggregating training information based on the contribution of the data and performance metrics.
Key Features and Innovation:
- Processor with circuits to aggregate neural network training information.
- Aggregation based on data contribution and performance metrics.
- Training and utilization of one or more neural networks.
Potential Applications: This technology can be applied in various fields such as healthcare, finance, autonomous vehicles, and image recognition systems.
Problems Solved: The technology addresses the need for efficient neural network training and performance evaluation, enhancing the overall effectiveness of neural network applications.
Benefits:
- Improved training efficiency.
- Enhanced performance evaluation.
- Increased accuracy of neural network applications.
Commercial Applications: The technology can be utilized in industries such as healthcare for medical image analysis, finance for fraud detection, and autonomous vehicles for object recognition.
Prior Art: Readers can explore prior research on neural network training methods, performance evaluation techniques, and data aggregation in machine learning.
Frequently Updated Research: Stay updated on advancements in neural network training algorithms, performance metrics, and data aggregation strategies to enhance the technology's effectiveness.
Questions about Neural Network Training Technology: 1. What are the key benefits of aggregating neural network training information based on data contribution and performance metrics? 2. How can this technology be applied in real-world scenarios to improve efficiency and accuracy?
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Original Abstract Submitted
apparatuses, systems, and techniques to train/use one or more neural networks. in at least one embodiment, a processor comprises one or more circuits to cause neural network training information to be aggregated based, at least in part, on contribution of the neural network training data and one or more performance metrics of the neural network.
