Introduction & potential of federated learning

Federated learning opens up new paths in artificial intelligence and deep learning. Instead of collecting data centrally, models are trained further directly on the end devices – such as machines or sensors. This creates highly adapted models that continuously adjust to real conditions without sensitive data ever leaving the device.

This approach offers decisive advantages: maximum individualization, improved data security and compliance with regulatory requirements. In the webinar you will get a practical introduction to how federated learning works and learn how companies can increase their competitiveness with it. Application examples range from predictive maintenance and quality control in manufacturing to the optimization of complex supply chains.