Federated Learning
Federated learning is a distributed machine learning approach that trains algorithms on decentralized datasets residing on local devices, without exchanging the actual data. Instead of sending raw data to a central server, only model updates (like changes to weights) are shared and aggregated. This technique enhances privacy by keeping sensitive user data on their devices while still benefiting from collaborative model improvement.
Federated learning is like a group of chefs who learn to bake a perfect cake by anonymously sharing only their recipe adjustments, without ever revealing their secret ingredients.
Federated learning allows AI models to learn from vast amounts of private data distributed across many devices, enhancing privacy and reducing communication costs.
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