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 is then sent to participating devices. Each device independently trains the model using its own local data. After the local training is complete, the updated model parameters (or gradients) are sent back to the central server. The central server then aggregates these updates from multiple devices to improve the global model. This process is repeated iteratively until the global model reaches a desired level of performance.

Key advantages of verteiltlernen include enhanced data privacy, as sensitive raw data never leaves the user's device. It also reduces communication costs by only transmitting model updates rather than large datasets, and it enables training on data that is naturally distributed. However, challenges exist, such as managing device heterogeneity, handling unreliable network connections, and ensuring fairness in model performance across different devices. Distributed learning finds applications in areas like mobile keyboard prediction, healthcare, and financial fraud detection.