Federated Learning is Better with Non-Homomorphic Encryption-Reference-Cited by-同舟云学术

Federated Learning is Better with Non-Homomorphic Encryption

Published:2023-12-05 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 4th International Workshop on Distributed Machine Learning
language:
Short-container-title:

Author:

Burlachenko Konstantin¹^ORCID,Alrowithi Abdulmajeed²^ORCID,Albalawi Fahad Ali²^ORCID,Richtárik Peter¹^ORCID

Affiliation:

1. KAUST, Thuwal, Saudi Arabia

2. Saudi Data and AI Authority, Riyadh, Saudi Arabia

Funder

KAUST Baseline Research Scheme

SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3630048.3630182

Reference67 articles.

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3. Dan Alistarh , Demjan Grubic , Jerry Li , Ryota Tomioka , and Milan Vojnovic . 2017 . QSGD: Communication-efficient SGD via gradient quantization and encoding. Advances in neural information processing systems 30 (2017). Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic. 2017. QSGD: Communication-efficient SGD via gradient quantization and encoding. Advances in neural information processing systems 30 (2017).

4. Dan Alistarh , Torsten Hoefler , Mikael Johansson , Nikola Konstantinov , Sarit Khirirat , and Cédric Renggli . 2018. The convergence of sparsified gradient methods. Advances in Neural Information Processing Systems 31 ( 2018 ). Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat, and Cédric Renggli. 2018. The convergence of sparsified gradient methods. Advances in Neural Information Processing Systems 31 (2018).

5. Privacy-preserving deep learning via additively homomorphic encryption;Aono Yoshinori;IEEE Transactions on Information Forensics and Security,2017

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