Secure aggregation enhances federated learning security by:

The correct answer is C. Ensuring client contributions remain confidential even if the server is compromised. In federated learning, individual clients train local models and send updates (gradients) to a central server for aggregation. Secure aggregation is a cryptographic protocol that ensures client contributions remain confidential even if the server is compromised - the server can…

AI Security Design and Implementation

Question

Options

AEncrypting individual model updates so only the server can access them
BApplying differential privacy to training data
CEnsuring client contributions remain confidential even if the server is compromised
DProcessing client updates in isolation

How the community answered

(45 responses)

A
2% (1)
B
7% (3)
C
89% (40)
D
2% (1)

Explanation

In federated learning, individual clients train local models and send updates (gradients) to a central server for aggregation. Secure aggregation is a cryptographic protocol that ensures client contributions remain confidential even if the server is compromised - the server can compute the aggregate result but cannot inspect individual client updates. This is distinct from encrypting updates only the server can read (A), which would not protect against a compromised server. Differential privacy (B) adds noise to data but is a separate technique. Processing updates in isolation (D) does not provide the cryptographic confidentiality guarantees that secure aggregation does.

Topics

#Federated Learning#Secure Aggregation#Privacy-Preserving AI#Confidentiality

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