DPFLA: Defending Private Federated Learning Against Poisoning Attacks-Reference-Cited by-同舟云学术

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DPFLA: Defending Private Federated Learning Against Poisoning Attacks

Published:2024-07 Issue:4 Volume:17 Page:1480-1491
ISSN:1939-1374
Container-title:IEEE Transactions on Services Computing
language:
Short-container-title:IEEE Trans. Serv. Comput.

Author:

Feng Xia¹^ORCID,Cheng Wenhao¹^ORCID,Cao Chunjie²^ORCID,Wang Liangmin³^ORCID,Sheng Victor S.⁴^ORCID

Affiliation:

1. Faculty of Data Science, City University of Macau, Macau, China

2. School of Cyberspace Security, Hainan University, Haikou, China

3. School of Cyber Science and Engineering, Southeast University, Nanjing, China

4. Department of Computer Science, Texas Tech University, Lubbock, TX, USA

Funder

National Natural Science Foundation of China

Leading-edge Technology Program of Jiangsu Natural Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/4629386/10630587/10470437.pdf?arnumber=10470437

Reference33 articles.

1. Federated Learning: Challenges, Methods, and Future Directions

2. Federated Learning for Healthcare Informatics

3. Federated Learning for Open Banking

4. Privacy-Preserved Federated Learning for Autonomous Driving

5. Federated Learning in Smart City Sensing: Challenges and Opportunities

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. DGGI: Deep Generative Gradient Inversion with diffusion model;Information Fusion;2025-01

2. A survey on privacy-preserving federated learning against poisoning attacks;Cluster Computing;2024-07-01

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