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Federated Learning
Federated learning is a decentralized machine learning approach where models are trained on separate local datasets. Instead of centralizing raw data, only model updates (like gradients) are shared and aggregated on a central server. This allows for collaborative model training without compromising the privacy of individual data sources.
In plain terms
Imagine a group of cooks sharing their recipe improvements without ever revealing their secret ingredients list to each other.
Why it matters
It allows AI systems to learn from vast amounts of distributed data while upholding strict privacy and security requirements.
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