Beskar combines one-round post-quantum secure aggregation with precomputed signatures and masks, plus differential privacy at multiple stages, to protect gradients, intermediate models, and deployed models in federated learning.
Practical secure aggregation for privacy-preserving machine learning,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CR 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Efficient Full-Stack Private Federated Deep Learning with Post-Quantum Security
Beskar combines one-round post-quantum secure aggregation with precomputed signatures and masks, plus differential privacy at multiple stages, to protect gradients, intermediate models, and deployed models in federated learning.