Verteiltlernen
Verteiltlernen, also known as Federated Learning, is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging their data. This approach contrasts with traditional centralized machine learning, where all training data is pooled in a central server.
The core principle of verteiltlernen involves training a global model on a central server. This global model
Key advantages of verteiltlernen include enhanced data privacy, as sensitive raw data never leaves the user's