ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations-Reference-Cited by-同舟云学术

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ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations

Published:2024-06-04 Issue: Volume: Page:
ISSN:
Container-title:2024 IEEE 25th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)
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Author:

Ling Xinpeng¹,Fu Jie¹,Wang Kuncan¹,Liu Haitao¹,Chen Zhili¹

Affiliation:

1. East China Normal University,Shanghai Key Laboratory of Trustworthy Computing,Shanghai,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10579075/10579079/10579181.pdf?arnumber=10579181

Reference47 articles.

1. Communication-efficient learning of deep networks from decentralized data;McMahan,2017

2. Deep leakage from gradients;Zhu;Advances in neural information processing systems,2019

3. Exploiting Unintended Feature Leakage in Collaborative Learning

4. Learning rate adaptation for differentially private stochastic gradient descent;Koskela,2018

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Communication-Efficient and Private Federated Learning with Adaptive Sparsity-Based Pruning on Edge Computing;Electronics;2024-08-29

2. Seleção de Clientes Adaptativa baseada em Privacidade Diferencial para Aprendizado Federado;Anais Estendidos do XLII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC 2024);2024-05-20

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