# Training AI Models Together with Unlabeled Data Using a Teacher > 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. - **Patent:** US 20220012637 - **Original title:** Federated teacher-student machine learning - **Owner:** Nokia Technologies Oy - **Status:** Active - **Times cited:** 37 - **Field:** ai_ml, telecommunications, software, consumer_electronics, edge_computing ## 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. ## The clever bit The clever part is combining federated learning, where multiple devices collaboratively train an AI model without sharing raw data, with a 'teacher-student' approach that generates its own labels for previously unlabeled data. This allows the system to learn from much more data without needing expensive human labeling or compromising privacy. ## Real-world examples 1. AI models on smartphones learning to improve photo recognition without uploading personal photos. 2. IoT devices collaboratively learning to detect anomalies in sensor data. 3. Edge computing devices improving predictive maintenance models. 4. Healthcare systems training AI on patient data while maintaining privacy. ## Why it matters This technology is important for training powerful AI models while protecting user privacy. By allowing AI models to learn from unlabeled data directly on devices (like smartphones or IoT sensors) and only share model updates, it reduces the need to send sensitive raw data to a central server. This approach can also make AI training more efficient, as it reduces the massive human effort typically required to manually label vast amounts of data. ## Frequently asked questions ### What does Training AI Models Together with Unlabeled Data Using a Teacher cover? 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. ### Who owns patent US 20220012637? This patent is owned by Nokia Technologies Oy. ### When does this patent expire? This patent is expected to expire on July 8, 2041, when the invention enters the public domain. ### What is patent US 20220012637 cited by? This patent has been cited by 37 later patents that build on its ideas. ### What problem does this patent solve? This technology is important for training powerful AI models while protecting user privacy. By allowing AI models to learn from unlabeled data directly on devices (like smartphones or IoT sensors) and only share model updates, it reduces the need to send sensitive raw data to a central server. This approach can also make AI training more efficient, as it reduces the massive human effort typically required to manually label vast amounts of data. ### What does this patent NOT cover? Does not cover federated learning systems that rely solely on manually labeled data for training. **Full plain-English explainer:** https://patentbrief.org/patent/us/20220012637/federated-teacher-student-machine-learning **Original patent:** https://patents.google.com/patent/US20220012637 --- _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. - [Training AI on Private Data Without Seeing It](https://patentbrief.org/patent/us/12518214/distributed-machine-learning-systems-including-generation-of-synthetic-data) — This patent describes a way to train artificial intelligence models using private data stored on many separate computers, by generating fake data that mimics the real data's patterns, so the private data itself never leaves its original location. - [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. - [How AI Learns New Tasks Using Old Data Labels](https://patentbrief.org/patent/us/11062228/gpt-3-few-shot-learning) — A method for helping AI models understand new topics by grouping similar labels from different datasets into a shared, broader category. - [How Google Distributed Machine Learning Across Many Computers](https://patentbrief.org/patent/us/7222127/google-adsense) — A 2003 Google patent describing a way to build machine learning models by splitting the work across a large network of computers rather than a single machine.