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SPEED: Secure, PrivatE, and Efficient Deep learning
v1v2 (latest)

SPEED: Secure, PrivatE, and Efficient Deep learning

16 June 2020
Arnaud Grivet Sébert
Rafael Pinot
Martin Zuber
Cédric Gouy-Pailler
Renaud Sirdey
    FedML
ArXiv (abs)PDFHTML

Papers citing "SPEED: Secure, PrivatE, and Efficient Deep learning"

6 / 6 papers shown
Efficient Decoding Methods for Language Models on Encrypted Data
Efficient Decoding Methods for Language Models on Encrypted Data
Matan Avitan
Moran Baruch
Nir Drucker
Itamar Zimerman
Yoav Goldberg
201
3
0
10 Sep 2025
Approximate Agreement Algorithms for Byzantine Collaborative Learning
Approximate Agreement Algorithms for Byzantine Collaborative LearningACM Symposium on Parallelism in Algorithms and Architectures (SPAA), 2025
Tijana Milentijević
Mélanie Cambus
Darya Melnyk
Stefan Schmid
FedML
460
3
0
02 Apr 2025
SABLE: Secure And Byzantine robust LEarning
SABLE: Secure And Byzantine robust LEarning
Antoine Choffrut
R. Guerraoui
Rafael Pinot
Renaud Sirdey
John Stephan
Martin Zuber
AAML
473
2
0
11 Sep 2023
When approximate design for fast homomorphic computation provides
  differential privacy guarantees
When approximate design for fast homomorphic computation provides differential privacy guarantees
Arnaud Grivet Sébert
Martin Zuber
Oana Stan
Renaud Sirdey
Cédric Gouy-Pailler
TPM
159
2
0
06 Apr 2023
Private and Reliable Neural Network Inference
Private and Reliable Neural Network InferenceConference on Computer and Communications Security (CCS), 2022
Nikola Jovanović
Marc Fischer
Samuel Steffen
Martin Vechev
303
22
0
27 Oct 2022
Protecting Data from all Parties: Combining FHE and DP in Federated
  Learning
Protecting Data from all Parties: Combining FHE and DP in Federated Learning
Arnaud Grivet Sébert
Renaud Sirdey
Oana Stan
Cédric Gouy-Pailler
FedML
177
0
0
09 May 2022
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