Federated Learning for Pedestrian Detection in Vehicular Networks-Reference-Cited by-同舟云学术

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Federated Learning for Pedestrian Detection in Vehicular Networks

Published:2023-07-04 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)
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Author:

Kumec Feyzi Ege¹,Reyhanoglu Aslihan¹,Kar Emrah¹,Turan Bugra²,Coleri Sinem²,Bennis Mehdi³,Elgabli Anis³,Gunduz Deniz⁴,Karaagac Sercan⁵

Affiliation:

1. Koc University Ford Otosan Automotive Technologies Laboratory (KUFOTAL), Sariyer,Istanbul,Turkey,34450

2. Koc University, Sariyer,Department of Electrical and Electronics Engineering,Istanbul,Turkey,34450

3. University of Oulu,Centre for Wireless Communications

4. Imperial College London,Department of Electrical and Electronic Engineering,London,UK

5. Ford Otosan, Sancaktepe,Istanbul,Turkey,34885

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10299690/10299691/10299783.pdf?arnumber=10299783

Reference21 articles.

1. Federated Learning in Vehicular Networks: Opportunities and Solutions

2. Federated Learning in Vehicular Networks

3. OES-Fed: a federated learning framework in vehicular network based on noise data filtering

4. Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications

5. Federated learning of deep networks using model averaging;mcmahan;CoRR,2016

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