Federated Reinforcement Learning (Glossary)

Federated Reinforcement Learning is primarily applied in the fields of Artificial Intelligence, Big Data and Smart Data, and Automation.

The term describes a special form of machine learning where different computers, devices, or even entire companies learn together from their data without sharing that data. The goal is to train algorithms so that they make optimal decisions through trial and error – and in a decentralised manner, meaning distributed across many different devices. „Federated“ means that each participant keeps their own data and only shares what they have learned with others. This protects privacy and reduces security risks.

A vivid example: Several factories each use their own robots for quality control. With Federated Reinforcement Learning, all robots can learn from each other how to detect defects better – without sensitive production data needing to be exchanged between the factories.

This makes the method particularly attractive for companies that want to benefit from data collectively but do not want to disclose sensitive trade secrets. This way, many small learning experiences lead to great collective progress in the field of automation and artificial intelligence.