Designed shifts that make the eavesdropper's Fisher information matrix singular hide one component of a federated model from network eavesdroppers.
Practical secure aggregation for privacy-preserving machine learning,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
ModShift: Model Privacy via Designed Shifts
Designed shifts that make the eavesdropper's Fisher information matrix singular hide one component of a federated model from network eavesdroppers.