Distributed Learning

Modern Artificial Intelligence (AI) methods are a powerful tool to address the complex problems in our society. In this process, ever-deeper neural networks also require ever-larger and more diverse data sets. This alone presents a huge challenge. As a result, data-processing companies are collecting more and more data for AI training. While it is currently standard to send all data to a central cloud, Fraunhofer IMS is working on a different solution - distributed learning, even working on resource-restricted embedded systems.

  • Data remains in your own hands

In healthcare and other fields of application, data protection is a decisive criterion. Legal requirements and also the information culture of companies prohibit sharing data with other participants.

Therefore, in distributed learning the network comes to the user. Sensitive data is used for training on the user's own devices. Afterwards only the learning success achieved is transmitted, so that all participants do not have to give away their own data.

  • Profit from the knowledge of others

After the training the individual learning progresses are merged into a common model. This is then distributed again. In this way each participant benefits from the knowledge of the others without revealing their own data.

  • Application in real time

A decisive advantage is additionally that the participants can learn independently from each other. We are working on making this vision a reality. One example is handwriting recognition on multiple devices. The goal is to recognize characters such as letters and numbers. Even if all participants are taught different numbers, in the end everyone benefits from the distributed learning approach and can recognize all numbers correctly. In this way more data can be recorded and processed even on small scales and within a shorter period of time. This is because in real-time applications bandwidths and latency limit the use of cloud solutions.

  • Fraunhofer IMS stands up for your data

Fraunhofer IMS is working on reducing the communication effort even further without having to accept a noticeable reduction in the quality of the common model. Furthermore, research is being conducted on methods to additionally increase resilience. All this enables the use of distributed learning in a data-sensitive environment.

 Comparison of distributed learning and conventional
© Fraunhofer IMS
Contrasting distributed learning and conventional learning when dealing with personal data

Our technologies - Innovations for your products

Hybrid Learning and PGNNs

In case of insufficient training data simulations based on physical models help to improve the data base.

Feature Extraction for Smart Sensors

By means of adapted feature extraction the size of a neural network can be reduced.

Embedded AI for LiDAR

Embedded AI can be used to accelerate and improve the quality of distance measurement by LiDAR sensors.

Industry 4.0 Research

Against the background of "Industry 4.0", Fraunhofer IMS is researching predictive maintenance solutions for the manufacturing industry.

Our technology areas - Our technologies for your development

Communication and Networking

Communication interfaces allow data exchange with other devices and connection to networks

User Interfaces

User interfaces as interface between device and user allow the configuration and operation of a product

Machine Learning for Embedded Systems

Artificial intelligence on resource-limited systems can be used to extract higher quality information from raw sensor data

Computer Vision

Computer Vision methods extract the maximum amount of information from image data


Embedded Software and Artificial Intelligence (Home)

Here you can get back to the overview page of the core competence Embedded Software and Artificial Intelligence (ESA).