FedDef: Defense Against Gradient Leakage in Federated Learning-based
Network Intrusion Detection Systems

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FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems

IEEE Transactions on Information Forensics and Security (IEEE TIFS), 2022

8 October 2022

Qi Li

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Papers citing "FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems"

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Title
An Evaluation Framework for Network IDS/IPS Datasets: Leveraging MITRE ATT&CK and Industry Relevance Metrics Adrita Rahman Tori Khondokar Fida Hasan 72 1 0 16 Nov 2025
On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions Daniel Gutiérrez Yelizaveta Falkouskaya Jose L. Hernandez-Ramos Aris Anagnostopoulos I. Chatzigiannakis A. Vitaletti FedML 120 2 0 19 Aug 2025
Adaptive Federated Learning with Functional Encryption: A Comparison of Classical and Quantum-safe Options Enrico Sorbera Federica Zanetti Giacomo Brandi Alessandro Tomasi Roberto Doriguzzi-Corin Silvio Ranise FedML 318 3 0 01 Apr 2025
Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach Qi Tan Qi Li Yi Zhao Zhuotao Liu Xiaobing Guo Ke Xu FedML 193 8 0 02 Mar 2024