LFGurad: A Defense against Label Flipping Attack in Federated Learning for Vehicular Network-Reference-Cited by-同舟云学术

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LFGurad: A Defense against Label Flipping Attack in Federated Learning for Vehicular Network

Published:2024-12 Issue: Volume:254 Page:110768
ISSN:1389-1286
Container-title:Computer Networks
language:en
Short-container-title:Computer Networks

Author:

K.M. Sameera^ORCID,P. Vinod,K.A. Rafidha Rehiman^ORCID,Conti Mauro^ORCID

Funder

Horizon Europe

Publisher

Elsevier BV

Reference71 articles.

1. Smart Direct-LTE communication: An energy saving perspective;Mumtaz;Ad Hoc Netw.,2014

2. Artificial intelligence for edge service optimization in internet of vehicles: A survey;Xu;Tsinghua Sci. Technol.,2021

3. A survey on recent advances in vehicular network security, trust, and privacy;Lu;IEEE Trans. Intell. Transp. Syst.,2018

4. Novel deep learning-enabled LSTM autoencoder architecture for discovering anomalous events from intelligent transportation systems;Ashraf;IEEE Trans. Intell. Transp. Syst.,2020

5. FedParking: A federated learning based parking space estimation with parked vehicle assisted edge computing;Huang;IEEE Trans. Veh. Technol.,2021

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