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Federated Learning for Privacy-Preserving AI
Federated Learning is a decentralized machine learning approach where models are trained collaboratively across multiple data sources, such as mobile devices or hospitals, without centralizing the raw data. Instead, only model updates or gradients are shared with a central server, which aggregates them to improve a global model while keeping sensitive user data local.
In plain terms
It's like a group of chefs collaboratively improving a recipe by sharing their updated techniques, without ever revealing their secret ingredient stashes to each other.
Why it matters
It allows AI to learn from diverse, confidential datasets while upholding strict privacy and data security regulations.
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