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Federated Learning
Federated Learning
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Federated learning trains a model across decentralized devices while keeping data local. What is the main privacy benefit?
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A.
Raw user data never leaves the device; only model updates (gradients or parameters) are transmitted to the server
B.
All devices share an encrypted copy of the training data, which is decrypted only on the central server for aggregation
C.
Each device runs inference but not training, so no gradients are computed or shared at all
D.
The central server owns the data but must follow privacy policies, and federated learning is a compliance framework for data handling
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