{
  "patent_number": "US 20220012637",
  "country": "US",
  "title": "Training AI Models Together with Unlabeled Data Using a Teacher",
  "original_title": "Federated teacher-student machine learning",
  "summary": "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.",
  "what_it_does": "This patent describes an apparatus, or node, within a federated machine learning system. This node contains a 'federated student machine learning network' that updates its own AI model by considering the updated models from other nodes in the system (Claim 1). Crucially, it also has a 'teacher machine learning network' which receives data that has not been manually labeled. The teacher network then creates 'pseudo-labels' for this unlabeled data (Claim 1). The federated student network then uses this unlabeled data along with the teacher's pseudo-labels to perform supervised learning (Claim 1). For example, a network on a phone could learn to identify new types of objects in photos by getting rough labels from a local 'teacher' AI, while also sharing its learning with other phones to improve overall accuracy without sending private photos to a central server.",
  "what_it_does_not_cover": [
    "Does not cover federated learning systems that rely solely on manually labeled data for training.",
    "Does not cover machine learning systems where a 'student' network does not update its model based on other 'nodes' in a federated system.",
    "Does not cover traditional centralized machine learning where all data is sent to one server for training.",
    "Does not cover systems that use a teacher network but do not involve a federated student network.",
    "Does not cover systems that only use unsupervised learning without generating pseudo-labels for supervised learning."
  ],
  "filed": "2021-07-08",
  "granted": null,
  "expires": "2041-07-08",
  "status": "active",
  "holder": "Nokia Technologies Oy",
  "holder_url": "https://patentbrief.org/company/nokia-technologies-oy",
  "inventors": [
    {
      "name": "Francesco Cricri",
      "url": "https://patentbrief.org/inventor/francesco-cricri"
    },
    {
      "name": "Hamed Rezazadegan Tavakoli",
      "url": "https://patentbrief.org/inventor/hamed-rezazadegan-tavakoli"
    },
    {
      "name": "Emre Baris Aksu",
      "url": "https://patentbrief.org/inventor/emre-baris-aksu"
    }
  ],
  "times_cited": 37,
  "tags": [
    "ai_ml",
    "telecommunications",
    "software",
    "consumer_electronics",
    "edge_computing"
  ],
  "abstract": "A node for a federated machine learning system that comprises the node and one or more other nodes configured for the same machine learning task, the node comprising:a federated student machine learning network configured to update a machine learning model in dependence upon updated machine learning models of the one or more node;a teacher machine learning network;means for receiving unlabeled data;means for teaching, using supervised learning, at least the federated first machine learning network using the teacher machine learning network, wherein the teacher machine learning network is configured to receive the data and produce pseudo labels for supervised learning using the data and wherein the federated student machine learning network is configured to perform supervised learning in dependence upon the same received data and the pseudo-labels.",
  "url": "https://patentbrief.org/patent/us/20220012637/federated-teacher-student-machine-learning",
  "markdown_url": "https://patentbrief.org/patent/us/20220012637/federated-teacher-student-machine-learning/md",
  "google_patents_url": "https://patents.google.com/patent/US20220012637",
  "relatedPatents": [
    {
      "patentNumber": "12443890",
      "countryCode": "US",
      "title": "How Devices Train Shared AI Models While Keeping Your Data Private",
      "url": "https://patentbrief.org/patent/us/12443890/partially-local-federated-learning"
    },
    {
      "patentNumber": "12518214",
      "countryCode": "US",
      "title": "Training AI on Private Data Without Seeing It",
      "url": "https://patentbrief.org/patent/us/12518214/distributed-machine-learning-systems-including-generation-of-synthetic-data"
    },
    {
      "patentNumber": "12574477",
      "countryCode": "US",
      "title": "Training AI Models Across Different Computers",
      "url": "https://patentbrief.org/patent/us/12574477/distributed-deep-learning-using-a-distributed-deep-neural-network"
    },
    {
      "patentNumber": "11062228",
      "countryCode": "US",
      "title": "How AI Learns New Tasks Using Old Data Labels",
      "url": "https://patentbrief.org/patent/us/11062228/gpt-3-few-shot-learning"
    },
    {
      "patentNumber": "7222127",
      "countryCode": "US",
      "title": "How Google Distributed Machine Learning Across Many Computers",
      "url": "https://patentbrief.org/patent/us/7222127/google-adsense"
    }
  ]
}