# 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. - **Patent:** US 12626144 - **Original title:** Data processing method, federated learning training method, and related apparatus and device - **Owner:** Huawei Technologies Co - **Granted:** 2026 - **Status:** Active - **Times cited:** 0 - **Field:** software, telecommunications, ai_ml, consumer_electronics ## What it does 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. ## 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. ## 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. ## Real-world examples 1. Secure training of AI models on mobile devices 2. Collaborative machine learning for smart home devices 3. Privacy-preserving AI for healthcare applications 4. Federated learning systems for edge computing ## Why it matters 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. ## 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. **Full plain-English explainer:** https://patentbrief.org/patent/us/12626144/data-processing-method-federated-learning-training-method-and-related-apparatus- **Original patent:** https://patents.google.com/patent/US12626144 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [How Devices Train Shared AI Models While Keeping Your Data Private](https://patentbrief.org/patent/us/12443890/partially-local-federated-learning) — This patent describes a method for training a machine learning model across many devices, where each device keeps some parts of the model and its data private, only sharing updates for the common, global parts of the model. - [How AI Models Learn from Your Health Data Without Sharing It](https://patentbrief.org/patent/us/11853891/system-and-method-with-federated-learning-model-for-medical-research-application) — This patent describes a system where personal health data, like photos or audio, stays on your device while helping train a shared AI model for medical research, improving predictions without sending your raw data. - [How Distributed AI Models Learn Privately on Edge Devices](https://patentbrief.org/patent/us/20260197424/distributed-deep-learning-using-a-distributed-deep-neural-network-20260197424) — This patent describes how a central computer can train a basic artificial intelligence model, send it to a remote device, and let that device customize the model using its own private data, then use it to filter information before sending it back. - [Training AI Models Across Different Computers](https://patentbrief.org/patent/us/12574477/distributed-deep-learning-using-a-distributed-deep-neural-network) — This 2026 patent describes a way to train AI models on one computer, send a version to another computer for further training with private data, and then update the original model with the improvements. - [Training AI Models Together with Unlabeled Data Using a Teacher](https://patentbrief.org/patent/us/20220012637/federated-teacher-student-machine-learning) — This patent describes a way for multiple AI systems to learn together from data that hasn't been manually labeled, using a 'teacher' AI to create temporary labels for a 'student' AI.