Careful What You Wish For: on the Extraction of Adversarially Trained Models
Authors: Kacem Khaled, Gabriela Nicolescu, Felipe Gohring de Magalhães | Published: 2022-07-21
ポイズニング
MUD-PQFed: Towards Malicious User Detection in Privacy-Preserving Quantized Federated Learning
Authors: Hua Ma, Qun Li, Yifeng Zheng, Zhi Zhang, Xiaoning Liu, Yansong Gao, Said F. Al-Sarawi, Derek Abbott | Published: 2022-07-19
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications
Authors: Ali Raza, Shujun Li, Kim-Phuc Tran, Ludovic Koehl, Kim Duc Tran | Published: 2022-07-18 | Updated: 2025-03-25
Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware
Authors: Luca Demetrio, Battista Biggio, Fabio Roli | Published: 2022-07-12
Efficient and Privacy Preserving Group Signature for Federated Learning
Authors: Sneha Kanchan, Jae Won Jang, Jun Yong Yoon, Bong Jun Choi | Published: 2022-07-12 | Updated: 2022-07-15
Statistical Detection of Adversarial examples in Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systems
Authors: Ibrahim Aliyu, Selinde van Engelenburg, Muhammed Bashir Muazu, Jinsul Kim, Chang Gyoon Lim | Published: 2022-07-11
Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms
Authors: Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif | Published: 2022-07-05
Defending against the Label-flipping Attack in Federated Learning
Authors: Najeeb Moharram Jebreel, Josep Domingo-Ferrer, David Sánchez, Alberto Blanco-Justicia | Published: 2022-07-05
FL-Defender: Combating Targeted Attacks in Federated Learning
Authors: Najeeb Jebreel, Josep Domingo-Ferrer | Published: 2022-07-02
I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defences
Authors: Daryna Oliynyk, Rudolf Mayer, Andreas Rauber | Published: 2022-06-16 | Updated: 2023-06-06