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Federated learning

Apprendimento federato

Federated learning is a technique for training a shared model without ever gathering the raw data in one place: the models travel, not the personal information. Each device, for instance your phone, locally trains a copy of the model on its own data and sends the central server only the learned updates, not the data itself. The server merges them into a better model and redistributes it. It is like cooks in different kitchens exchanging only recipe improvements, never sharing their private home ingredients. Sensitive data stays where it was born, under the control of its owner.

Definition

Federated learning matters because it lets you exploit vast distributed data while protecting privacy and respecting rules like the GDPR. It is used for predictive keyboards, healthcare and finance, where centralizing data would be risky or forbidden.

Related terms

• Machine Learning
• Edge AI
• Deep Learning
• Synthetic Data
• Compute

More in AI Fundamentals

• LLM (Large Language Model)
• Token
• Context Window
• Temperature
• AI Hallucination
• Fine-tuning
• RAG (Retrieval Augmented Generation)
• Embedding

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