Impact of Neural Network Depth on Split Federated Learning Performance in Low-Resource UAV Networks-Reference-Cited by-同舟云学术

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Impact of Neural Network Depth on Split Federated Learning Performance in Low-Resource UAV Networks

Published:2024-05-27 Issue: Volume:abs/1812.03288 Page:1290-1295
ISSN:
Container-title:2024 International Wireless Communications and Mobile Computing (IWCMC)
language:
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Author:

Hafi Houda¹,Brik Bouziane²,Bagaa Miloud³,Ksentini Adlen⁴

Affiliation:

1. Abdelhamid Mehri University,NTIC Faculty,Constantine,Algeria

2. Sharjah University,College of Computing and Informatics,Computer Science Department,Sharjah,UAE

3. Universite du Quebec a Trois-Rivieres,Trois-Rivieres,QC,Canada

4. EURECOM,Sophia-Antipolis,France

Funder

AC3

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10592105/10592309/10592346.pdf?arnumber=10592346

Reference15 articles.

1. Federated Learning for UAVs-Enabled Wireless Networks: Use Cases, Challenges, and Open Problems

2. A Survey on Federated Learning for Resource-Constrained IoT Devices

3. No peek: A survey of private distributed deep learning;Vepakomma;CoRR,2018

4. SplitFed: When Federated Learning Meets Split Learning

5. Binarizing Split Learning for Data Privacy Enhancement and Computation Reduction

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