How Devices Securely Share Machine Learning Model Updates
This patent describes a method for multiple devices to train a machine learning model using their own data and then securely share parts of the trained model with each other before sending it to a central server, improving data privacy.
Original patent title: “Data processing method, federated learning training method, and related apparatus and device”
This patent describes a method for multiple devices to train a machine learning model using their own data and then securely share parts of the trained model with each other before sending it to a central server, improving data privacy. Granted to Huawei Technologies Co in 2026 with 24 claims, and it is expected to expire in 2042.
Coverage
What does this patent actually cover?
This patent details a process for training machine learning models across many devices, like smartphones or computers, without sending all the raw data to a central server. A 'first terminal' (one of the devices) gets a model from a server, trains it using its own local data to create 'trained model parameters,' and then figures out which other devices ('first collaborative terminal') it should work with based on a 'collaboration relationship' provided by the server. It then splits its trained model parameters, encrypts parts of them with 'first encryption noise,' and sends these encrypted parts to its collaborative terminal(s). The collaborative terminal(s) also encrypt their received parts with 'second encryption noise.' Finally, these encrypted pieces are sent to the main server, which combines them to build a better, more accurate machine learning model. This approach aims to make data exchange more secure.
The gap
What does this patent NOT cover?
- Does not cover methods where devices send all their local data to the server.
- Does not cover training methods that do not involve a server coordinating the process.
- Does not cover scenarios where devices do not identify or collaborate with other specific devices for parameter sharing.
- Does not cover methods where the model parameters are not encrypted with noise before transmission.
- Does not cover systems where the 'collaboration relationship' is not provided by the server.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The innovation lies in the multi-stage encryption and collaborative sharing of model parameters between devices before they reach the central server. By splitting parameters and encrypting them with different 'noise' at both the originating and collaborative terminals, it creates a more robust privacy shield than simply encrypting once.
The Patent Drawing
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Secure training of AI models on mobile devices
Collaborative machine learning for smart home devices
Privacy-preserving AI for healthcare applications
Federated learning systems for edge computing
Why it matters
The bigger picture
This patent is part of the growing field of federated learning, which is crucial for training AI models on sensitive data like personal health information or user behavior without compromising privacy. It offers a technical solution to enhance the security of data exchange in such distributed learning systems.
Filed
November 18, 2022
Granted
May 12, 2026
Market context
Who's building on this
Companies in this space
Companies like Google, Apple, and Meta are heavily invested in federated learning technologies for their AI services. Startups in the privacy-enhancing computation space are also exploring similar distributed training methods.
Market impact
This patent contributes to the development of privacy-preserving AI, a rapidly expanding market driven by increasing data privacy regulations and user demand for secure services. It provides a framework that could enable more sophisticated AI applications without centralizing sensitive user data.
Claim 1 — Plain English
What this patent covers
This patent details a process for training machine learning models across many devices, like smartphones or computers, without sending all the raw data to a central server. A 'first terminal' (one of the devices) gets a model from a server, trains it using its own local data to create 'trained model parameters,' and then figures out which other devices ('first collaborative terminal') it should work with based on a 'collaboration relationship' provided by the server. It then splits its trained model parameters, encrypts parts of them with 'first encryption noise,' and sends these encrypted parts to its collaborative terminal(s). The collaborative terminal(s) also encrypt their received parts with 'second encryption noise.' Finally, these encrypted pieces are sent to the main server, which combines them to build a better, more accurate machine learning model. This approach aims to make data exchange more secure.
The clever bit
The innovation lies in the multi-stage encryption and collaborative sharing of model parameters between devices before they reach the central server. By splitting parameters and encrypting them with different 'noise' at both the originating and collaborative terminals, it creates a more robust privacy shield than simply encrypting once.
What it does not cover
- Does not cover methods where devices send all their local data to the server.
- Does not cover training methods that do not involve a server coordinating the process.
- Does not cover scenarios where devices do not identify or collaborate with other specific devices for parameter sharing.
- Does not cover methods where the model parameters are not encrypted with noise before transmission.
- Does not cover systems where the 'collaboration relationship' is not provided by the server.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
Patent enters public domain
PatentBrief Score
Impact Score
Moderate
Citation count
0/40
No citations yet
Claim breadth
16/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
20/20
Granted within 5 years
Assignee scale
20/20
Major company or institution
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$31K – $100K
Midpoint $62K · 16.1 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
Claim text not yet imported for this patent
The original legal language
Original claims
24 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Li, G., Shao, Y., & Zhang, L. (2026). How Devices Securely Share Machine Learning Model Updates (U.S. Patent No. 12,626,144). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/12626144/data-processing-method-federated-learning-training-method-and-related-apparatus-
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
Frequently Asked Questions
What does How Devices Securely Share Machine Learning Model Updates cover?
This patent describes a method for multiple devices to train a machine learning model using their own data and then securely share parts of the trained model with each other before sending it to a central server, improving data privacy.
Who owns patent US 12626144?
Huawei Technologies Co owns this patent, granted in 2026.
When does this patent expire?
This patent is expected to expire on November 18, 2042, when the invention enters the public domain.
What problem does this patent solve?
This patent is part of the growing field of federated learning, which is crucial for training AI models on sensitive data like personal health information or user behavior without compromising privacy. It offers a technical solution to enhance the security of data exchange in such distributed learning systems.
What does this patent NOT cover?
Does not cover methods where devices send all their local data to the server.
Patent monitoring