A survey on vulnerability of federated learning: A learning algorithm perspective-Reference-Cited by-同舟云学术

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A survey on vulnerability of federated learning: A learning algorithm perspective

Published:2024-03 Issue: Volume:573 Page:127225
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Xie Xianghua^ORCID,Hu Chen,Ren Hanchi,Deng Jingjing^ORCID

Publisher

Elsevier BV

Reference155 articles.

1. Improving language understanding by generative pre-training;Radford,2018

2. Language models are unsupervised multitask learners;Radford;OpenAI Blog,2019

3. Language models are few-shot learners;Brown,2020

4. Denoising diffusion probabilistic models;Ho,2020

5. Deep unsupervised learning using nonequilibrium thermodynamics;Sohl-Dickstein,2015

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3. Genomic privacy preservation in genome-wide association studies: taxonomy, limitations, challenges, and vision;Briefings in Bioinformatics;2024-07-25

4. Federated Learning for Credit Card Fraud Detection: Key Fundamentals and Emerging Trends;2024 International Conference on Circuit, Systems and Communication (ICCSC);2024-06-28

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