Nvidia patents method to weigh data from different devices in AI training
Nvidia describes a system for improving how artificial intelligence models learn from multiple devices without sharing raw data. A central server calculates how much each device's local updates should count in the final model, based on how much those updates changed during training.
In the filing’s own words
Apparatuses, systems, and techniques to improve federated learning for neural networks. In at least one embodiment, a federated server dynamically selects neural network weights according to one or more learnable aggregation weights indicating a contribution from each of one or more edge devices or clients during federated training according to various characteristics of each edge device or client model and training data.
What exactly they claim (claim 1)
1. A computer system comprising: one or more processors to: determine a weighting for each of one or more sets of neural network weights stored by one or more federated learning clients based, at least in part, on respective degrees of one or more changes to the one or more sets of neural network weights resulting from one or more training operations completed by the one or more federated learning clients; and apply the weightings to the one or more sets of neural network weights to generate one or more aggregated neural network weights to update a neural network.
Facts
- Company
- Nvidia (NVIDIA Corporation)
- Official title
- Dynamic weight updates for neural networks
- Patent number
- US12737588B2
- Application number
- 17244781
- Filed
- Apr 29, 2021
- Granted
- Sep 15, 2026
- Inventors
- 6
- Claims
- 37
Classified under
G06N 3/045 Physics & computingG06F 18/214 Physics & computingG06N 3/047 Physics & computingH04L 67/10 Electricity & electronicsH04L 67/34 Electricity & electronicsMore from Nvidia in AI & software
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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.