Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach

30 November 2023

Papers citing "Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach"

8 / 8 papers shown

Title
Undermining Federated Learning Accuracy in EdgeIoT via Variational Graph Auto-Encoders Kai Li Shuyan Hu Bochun Wu Sai Zou Wei Ni Falko Dressler AAML FedML 32 0 0 14 Apr 2025
Towards Resilient Federated Learning in CyberEdge Networks: Recent Advances and Future Trends Kai Li Zhengyang Zhang Azadeh Pourkabirian Wei Ni Falko Dressler Ozgur B. Akan 48 0 0 01 Apr 2025
A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT Kai Li Yilei Liang Xin Yuan Wei Ni Jon Crowcroft Chau Yuen Ozgur B. Akan FedML 25 3 0 02 Oct 2024
A Novel Defense Against Poisoning Attacks on Federated Learning: LayerCAM Augmented with Autoencoder Jingjing Zheng Xin Yuan Kai Li Wei Ni Eduardo Tovar Jon Crowcroft FedML AAML 40 0 0 02 Jun 2024
Leverage Variational Graph Representation For Model Poisoning on Federated Learning Kai Li Xinnan Yuan Jingjing Zheng Wei Ni Falko Dressler Abbas Jamalipour AAML FedML 30 5 0 23 Apr 2024
Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey Yichen Wan Youyang Qu Wei Ni Yong Xiang Longxiang Gao Ekram Hossain AAML 49 33 0 14 Dec 2023
Securing Federated Learning: A Covert Communication-based Approach Yuan-ai Xie Jiawen Kang Dusit Niyato Nguyen Thi Thanh Van Nguyen Cong Luong Zhixin Liu Han Yu FedML 42 25 0 05 Oct 2021
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark W. Schmidt 139 1,199 0 16 Aug 2016