{
  "patent_number": "US 12626144",
  "country": "US",
  "title": "How Devices Securely Share Machine Learning Model Updates",
  "original_title": "Data processing method, federated learning training method, and related apparatus and device",
  "summary": "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.",
  "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."
  ],
  "filed": "2022-11-18",
  "granted": "2026-05-12",
  "expires": "2042-11-18",
  "status": "active",
  "holder": "Huawei Technologies Co",
  "holder_url": "https://patentbrief.org/company/huawei-technologies-co",
  "inventors": [
    {
      "name": "Gang Li",
      "url": "https://patentbrief.org/inventor/gang-li"
    },
    {
      "name": "Yunfeng Shao",
      "url": "https://patentbrief.org/inventor/yunfeng-shao"
    },
    {
      "name": "Lei Zhang",
      "url": "https://patentbrief.org/inventor/lei-zhang"
    }
  ],
  "times_cited": 0,
  "tags": [
    "software",
    "telecommunications",
    "ai_ml",
    "consumer_electronics"
  ],
  "abstract": "The technology of this application relates to a training method that includes a first terminal obtaining a to-be-trained first machine learning model from the server. The first terminal is any one of a plurality of terminals. The first terminal trains the first machine learning model by using local data stored by the first terminal, to obtain trained model parameters. The first terminal determines, based on a collaboration relationship, a first collaborative terminal corresponding to the first terminal, and sends a part or all of the trained model parameters of the first terminal to the server by using the first collaborative terminal. The collaboration relationship is delivered by the server to the first terminal. The foregoing manner can improve security of data exchange between the server and the terminal.",
  "url": "https://patentbrief.org/patent/us/12626144/data-processing-method-federated-learning-training-method-and-related-apparatus-",
  "markdown_url": "https://patentbrief.org/patent/us/12626144/data-processing-method-federated-learning-training-method-and-related-apparatus-/md",
  "google_patents_url": "https://patents.google.com/patent/US12626144",
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}