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cs.AI2024
FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users
Yogachandran Rahulamathavan, Charuka Herath, Xiaolan Liu +2
The federated learning (FL) technique was developed to mitigate data privacy issues in the traditional machine learning paradigm. While FL ensures that a user's data always remain…
cs.AI2024
A Hybrid Training-time and Run-time Defense Against Adversarial Attacks in Modulation Classification
Lu Zhang, Sangarapillai Lambotharan, Gan Zheng +3
Motivated by the superior performance of deep learning in many applications including computer vision and natural language processing, several recent studies have focused on applyi…
cs.AI2024
Countermeasures Against Adversarial Examples in Radio Signal Classification
Lu Zhang, Sangarapillai Lambotharan, Gan Zheng +2
Deep learning algorithms have been shown to be powerful in many communication network design problems, including that in automatic modulation classification. However, they are vuln…