Secure Decentralized Aggregation to Prevent Membership Privacy Leakage in Edge-Based Federated Learning-Reference-Cited by-同舟云学术

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Secure Decentralized Aggregation to Prevent Membership Privacy Leakage in Edge-Based Federated Learning

Published:2024-05 Issue:3 Volume:11 Page:3105-3119
ISSN:2327-4697
Container-title:IEEE Transactions on Network Science and Engineering
language:
Short-container-title:IEEE Trans. Netw. Sci. Eng.

Author:

Shen Meng¹^ORCID,Wang Jing¹^ORCID,Zhang Jie²^ORCID,Zhao Qinglin³^ORCID,Peng Bohan¹^ORCID,Wu Tong¹^ORCID,Zhu Liehuang¹^ORCID,Xu Ke⁴^ORCID

Affiliation:

1. School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China

2. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

3. School of Computer Science and Engineering, Macau University of Science and Technology, Macau, China

4. Department of Computer Science, Tsinghua University, Beijing, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Beijing Nova Program

Beijing Natural Science Foundation

Science and Technology Development Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488902/10510356/10436636.pdf?arnumber=10436636

Reference32 articles.

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

2. Toward Robust Hierarchical Federated Learning in Internet of Vehicles

3. Recent Advances on Federated Learning for Cybersecurity and Cybersecurity for Federated Learning for Internet of Things

4. Federated learning for mobile keyboard prediction;Hard,2018

5. Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning

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. Decentralized Online Bandit Federated Learning Over Unbalanced Directed Networks;IEEE Transactions on Network Science and Engineering;2024-09

2. A Multifaceted Survey on Federated Learning: Fundamentals, Paradigm Shifts, Practical Issues, Recent Developments, Partnerships, Trade-Offs, Trustworthiness, and Ways Forward;IEEE Access;2024

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