What is Federated Learning?
A training approach where AI learns from data spread across many devices without the data ever leaving those devices.
Why It Matters
Federated learning improves AI models while preserving user privacy since raw data stays on each device.
Real-World Example
Your phone's keyboard improves its predictions using your typing patterns without uploading your messages.
“Understanding terms like Federated Learning matters because it helps you have better conversations with developers and make smarter decisions about your software. You do not need to be technical. You just need to know enough to ask the right questions.”
Related Terms
Edge AI
Running AI models directly on local devices like phones or sensors rather than in the cloud.
Training Data
The dataset used to teach an AI model patterns and knowledge during its initial training.
AI Safety
The field of research focused on ensuring AI systems behave as intended and do not cause harm.
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Related Terms
Training Data
The dataset used to teach an AI model patterns and knowledge during its initial training.
AI Safety
The field of research focused on ensuring AI systems behave as intended and do not cause harm.
Edge AI
Running AI models directly on local devices like phones or sensors rather than in the cloud.
Large Language Model (LLM)
An AI system trained on massive amounts of text that can understand and generate human language.
Transformer
A type of AI architecture that processes text by paying attention to relationships between all words at once, rather...
Attention Mechanism
A technique that lets AI models focus on the most relevant parts of the input when generating output.