Server-side metric privacy, with noise scaled by a distance between client model updates, improves federated learning accuracy over global differential privacy in their experiments, but the claimed privacy protection is not formally established.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
Metric Privacy in Federated Learning for Medical Imaging: Improving Convergence and Preventing Client Inference Attacks
Server-side metric privacy, with noise scaled by a distance between client model updates, improves federated learning accuracy over global differential privacy in their experiments, but the claimed privacy protection is not formally established.