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Nvidia patents system for training AI models across separate devices

Nvidia describes a method where multiple devices take turns updating a single machine learning model. Each device uses its own private data to improve the model, ensuring that no other device can access that specific information during the training process.

Why it mattersThis approach could help companies build smarter AI models without sharing sensitive user data, which is a major concern for privacy and security.

In the filing’s own words

Apparatuses, systems, and techniques to train a machine-learned model. In at least one embodiment, a plurality of training clients each obtain an exclusive right to update a model in turn, and each client trains said model with training data not accessible to other training clients.

What exactly they claim (claim 1)

1. A processor comprising one or more circuits to cause one or more neural networks to be at least partially trained based, at least in part, on the one or more neural networks being at least partially trained by a second processor.

Facts

Company
Nvidia (NVIDIA Corporation)
Official title
DISTRIBUTED NEURAL NETWORK TRAINING SYSTEM
Publication number
US20260288904A1
Application number
19362087
Filed
Oct 17, 2025
Published
Sep 24, 2026
Inventors
4
Claims
1

Classified under

G06F 18/2148 Physics & computingG06F 9/4881 Physics & computingG06N 3/045 Physics & computingG06N 3/08 Physics & computingG06V 10/955 Physics & computingG16H 30/40 Physics & computing
Read the full filing (official PDF) ↗ Open in the Patent Tracker

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Summaries are simplified from the filing’s own abstract and first claim. A patent filing is not a product announcement.
Patent data and drawings: U.S. Patent and Trademark Office (USPTO). Nebula is not affiliated with or endorsed by the USPTO.