Question

Within Federated Learning, what remains on the user&#x27;s device during model training?

Accepted Answer

Within Federated Learning, the raw training data remains on the user&#x27;s device during model training. Federated Learning is a machine learning technique that allows a global model to be trained across multiple decentralized devices or servers holding local data samples, without exchanging them. Instead of sending the raw data to a central server, each device trains a local model on its own data and then sends only the updated model parameters (e.g., weights and biases) to the central server. The central server aggregates these updates to create a better global model. This protects user privacy because the sensitive raw data never leaves the device.

Within Federated Learning, what remains on the user's device during model training?

Within Federated Learning, the raw training data remains on the user's device during model training. Federated Learning is a machine learning technique that allows a global model to be trained acr....

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Within Federated Learning, what remains on the user's device during model training?

Within Federated Learning, the raw training data remains on the user's device during model training. Federated Learning is a machine learning technique that allows a global model to be trained acr....