Efficient and Straggler-Resistant Homomorphic Encryption for Heterogeneous Federated Learning-Reference-Cited by-同舟云学术

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Efficient and Straggler-Resistant Homomorphic Encryption for Heterogeneous Federated Learning

Published:2024-05-20 Issue: Volume:13 Page:791-800
ISSN:
Container-title:IEEE INFOCOM 2024 - IEEE Conference on Computer Communications
language:
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Author:

Yan Nan¹,Li Yuqing¹,Chen Jing¹,Wang Xiong²,Hong Jianan³,He Kun¹,Wang Wei⁴

Affiliation:

1. Wuhan University,School of Cyber Science and Engineering,Wuhan,China

2. Huazhong University of Science and Technology,School of Computer Science and Technology,Wuhan,China

3. Shanghai Jiao Tong University,School of Cyber Science and Engineering,Shanghai,China

4. Hong Kong University of Science and Technology,Department of Computer Science and Engineering,Hong Kong

Funder

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10621050/10621073/10621440.pdf?arnumber=10621440

Reference50 articles.

1. Federated Machine Learning

2. Personalized Cross-Silo Federated Learning on Non-IID Data

3. Advances and Open Problems in Federated Learning

4. Privacy-preserving deep learning via additively homomorphic encryption;Aono

5. Deep Leakage from Gradients

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