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2107.12997
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Fully Homomorphically Encrypted Deep Learning as a Service
26 July 2021
George Onoufriou
Paul Mayfield
Georgios Leontidis
FedML
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Papers citing
"Fully Homomorphically Encrypted Deep Learning as a Service"
7 / 7 papers shown
Title
Enhancing Strawberry Yield Forecasting with Backcasted IoT Sensor Data and Machine Learning
Tewodros Alemu Ayall
Andy Li
Matthew Beddows
Milan Markovic
Georgios Leontidis
34
0
0
25 Apr 2025
SLIP: Securing LLMs IP Using Weights Decomposition
Yehonathan Refael
Adam Hakim
Lev Greenberg
T. Aviv
S. Lokam
Ben Fishman
Shachar Seidman
46
3
0
15 Jul 2024
HEQuant: Marrying Homomorphic Encryption and Quantization for Communication-Efficient Private Inference
Tianshi Xu
Meng Li
Runsheng Wang
47
1
0
29 Jan 2024
Generating One-Hot Maps under Encryption
E. Aharoni
Nir Drucker
Eyal Kushnir
Ramy Masalha
Hayim Shaul
14
4
0
11 Jun 2023
Data Privacy with Homomorphic Encryption in Neural Networks Training and Inference
Ivone Amorim
Eva Maia
Pedro Barbosa
Isabel Praça
21
3
0
03 May 2023
EDLaaS: Fully Homomorphic Encryption Over Neural Network Graphs for Vision and Private Strawberry Yield Forecasting
George Onoufriou
Marc Hanheide
Georgios Leontidis
FedML
24
4
0
26 Oct 2021
The Role of Cross-Silo Federated Learning in Facilitating Data Sharing in the Agri-Food Sector
A. Durrant
Milan Markovic
David Matthews
David May
J. Enright
Georgios Leontidis
FedML
32
69
0
14 Apr 2021
1