GFCL: A GRU-based Federated Continual Learning Framework against Data
Poisoning Attacks in IoV

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GFCL: A GRU-based Federated Continual Learning Framework against Data Poisoning Attacks in IoV

23 April 2022

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Papers citing "GFCL: A GRU-based Federated Continual Learning Framework against Data Poisoning Attacks in IoV"

8 / 8 papers shown

Title
Adversarial Attacks Against Deep Reinforcement Learning Framework in Internet of Vehicles Anum Talpur G. Mohan AAML 56 7 0 02 Aug 2021
Machine Learning for Security in Vehicular Networks: A Comprehensive Survey Anum Talpur M. Gurusamy 41 63 0 31 May 2021
Deep Reinforcement Learning for Autonomous Driving: A Survey B. R. Kiran Ibrahim Sobh V. Talpaert Patrick Mannion A. A. Sallab S. Yogamani P. Pérez 352 1,687 0 02 Feb 2020
Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning Inaam Ilahi Muhammad Usama Junaid Qadir M. Janjua Ala I. Al-Fuqaha D. Hoang Dusit Niyato AAML 127 135 0 27 Jan 2020
MULDEF: Multi-model-based Defense Against Adversarial Examples for Neural Networks Siwakorn Srisakaokul Yuhao Zhang Zexuan Zhong Wei Yang Tao Xie Bo Li AAML 53 19 0 31 Aug 2018
On Detecting Adversarial Perturbations J. H. Metzen Tim Genewein Volker Fischer Bastian Bischoff AAML 61 950 0 14 Feb 2017
Adversarial Attacks on Neural Network Policies Sandy Huang Nicolas Papernot Ian Goodfellow Yan Duan Pieter Abbeel MLAU AAML 99 837 0 08 Feb 2017
Universal adversarial perturbations Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi Omar Fawzi P. Frossard AAML 145 2,533 0 26 Oct 2016

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