Secure Quantized Training for Deep Learning
Files
(Published version)
Date
2022
Authors
Keller, M.
Sun, K.
Editors
K, K.
Jegelka, S.
Song, L.
Szepesvari, C.
Niu, G.
Sabato, S.
Jegelka, S.
Song, L.
Szepesvari, C.
Niu, G.
Sabato, S.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Proceedings of Machine Learning Research, 2022 / K, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., Sabato, S. (ed./s), pp.10912-10938
Statement of Responsibility
Conference Name
39th International Conference on Machine Learning, ICML 2022 (17 Jul 2022 - 23 Jul 2022 : Baltimore, Maryland, USA, PMLR 162)
Abstract
We implement training of neural networks in secure multi-party computation (MPC) using quantization commonly used in said setting. We are the first to present an MNIST classifier purely trained in MPC that comes within 0.2 percent of the accuracy of the same convolutional neural network trained via plaintext computation. More concretely, we have trained a network with two convolutional and two dense layers to 99.2% accuracy in 3.5 hours (under one hour for 99% accuracy). We have also implemented AlexNet for CIFAR-10, which converges in a few hours. We develop novel protocols for exponentiation and inverse square root. Finally, we present experiments in a range of MPC security models for up to ten parties, both with honest and dishonest majority as well as semi-honest and malicious security.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
Copyright 2022 by the author(s)