Federated Learning
Federated Learning is a decentralized training technique that trains machine learning models across multiple remote edge devices holding local data samples, without exchanging the data itself.
Frequently Asked Questions
How does federated learning maintain privacy?▼
Devices train local model updates on private local data, and only send model weights/gradients to a central server where updates are averaged.
What is Federated Averaging?▼
The mathematical algorithm used by the central server to combine the model updates sent by edge nodes to update the global model.
Quick Facts
- CategoryModel Training
- Key ApplicationPrivacy-first medical diagnostics, smartphone autocomplete tuning, and local data aggregation.
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