Federated learning is a training approach in which an AI model learns across many devices or sites without the raw data ever leaving them. Each participant trains locally and sends only model updates to a central server, which combines them. This protects privacy and suits, for example, hospitals that want to improve a diagnostic model together, or smartphone keyboards that learn word suggestions.
Data protection regulations are legal rules governing the handling of personal data. In the EU the General Data Protection Regulation (GDPR) is the key framework; among other things it requires a legal basis for every processing activity, data minimisation, transparency towards data subjects and data processing agreements with service providers. When using AI, organisations need to clarify which data ends up in inputs, knowledge sources and logs, and where it is processed.
Training data are the examples from which an AI model learns patterns: large text collections for language models, labelled images for image models. Their quality, diversity and recency largely determine how reliable and fair a model is and how up to date its knowledge will be. For organisations it also matters whether a provider uses the customer data they enter for further training.
Machine learning is a branch of artificial intelligence in which computers derive patterns from data instead of having every rule explicitly programmed. Depending on the task, models learn from labelled examples (supervised), from unlabelled data (unsupervised) or through rewards (reinforcement). Applications range from spam filters and demand forecasts to product recommendations. Quality depends heavily on the quantity and quality of the training data.
Synthetic data is generated artificially rather than collected in the real world, but mimics the patterns and properties of real data. It helps when genuine data is scarce, expensive or sensitive from a data protection perspective – for example for testing software, training models or checking a chatbot with realistic sample queries. It is important that it reflects reality closely enough and does not allow conclusions about real people.
