Federated Learning
Federated Learning is a machine learning training paradigm that allows models to be trained on decentralized datasets located on various devices or servers without the raw data ever leaving its local source. Instead of sending sensitive data to a central server, only model updates (like learned parameters) are aggregated and shared, preserving user privacy. This iterative process allows a global model to learn from many local datasets while keeping the data private.
Federated learning is like a cooking club where members share their perfected recipe adjustments with a central coordinator, but never share their secret ingredients.
It enables robust AI development using vast amounts of private user data, such as on mobile phones or in healthcare, without compromising privacy or regulatory compliance.
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