Private and Secure Machine Learning in Wireless Mobile Communication-Reference-Cited by-同舟云学术

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Private and Secure Machine Learning in Wireless Mobile Communication

Published:2023-11-13 Issue: Volume:15 Page:1-6
ISSN:
Container-title:2023 IEEE Future Networks World Forum (FNWF)
language:
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Author:

Sanon Sogo Pierre¹,Tetteh Josephine N. A.²,Reddy Rekha¹,Schotten Hans Dieter³

Affiliation:

1. German Research Center for Artificial Intelligence,Intelligent Networks Research Group,Kaiserslautern,D-67663

2. Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany Frankfurt

3. Institute for Wireless Communication and Navigation, RPTU Kaiserslautern-Landau,Kaiserslautern,D-67663

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10520248/10520339/10520497.pdf?arnumber=10520497

Reference41 articles.

1. Split learning for health: Distributed deep learning without sharing raw patient data;Vepakomma,2018

2. SplitFed: When Federated Learning Meets Split Learning

3. The limitations of fed-erated learning in sybil settings;Fung,2020

4. Backdoor Federated Learning-Based mmWave Beam Selection

Cited by 1 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. Preserving Privacy in the Age of Wireless Evolution: Differential Privacy in 5G, Beyond 5G, and 6G;2023 International Conference on Future Communications and Networks (FCN);2023-12-17

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