Privacy Inference Attack and Defense in Centralized and Federated Learning: A Comprehensive Survey-Reference-Cited by-同舟云学术

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Privacy Inference Attack and Defense in Centralized and Federated Learning: A Comprehensive Survey

Published:2024 Issue: Volume: Page:1-22
ISSN:2691-4581
Container-title:IEEE Transactions on Artificial Intelligence
language:
Short-container-title:IEEE Trans. Artif. Intell.

Author:

Rao Bosen¹,Zhang Jiale¹,Wu Di²,Zhu Chengcheng¹,Sun Xiaobing¹,Chen Bing³

Affiliation:

1. School of Information Engineering, Yangzhou University, Yangzhou, China

2. School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Australia

3. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9078688/9184921/10429780.pdf?arnumber=10429780

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. FedCrow: Federated-Learning-Based Data Privacy Preservation in Crowd Sensing;Applied Sciences;2024-05-31

2. Demystifying Impact of Key Hyper-Parameters in Federated Learning: A Case Study on CIFAR-10 and FashionMNIST;IEEE Access;2024

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