Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors

31 January 2022

Papers citing "Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors"

5 / 5 papers shown

Title
Enhanced Federated Anomaly Detection Through Autoencoders Using Summary Statistics-Based Thresholding Sofiane Laridi Gregory Palmer Kam-Ming Mark Tam FedML 21 2 0 11 Oct 2024
SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks Omid Tavallaie Kanchana Thilakarathna Suranga Seneviratne Aruna Seneviratne Albert Y. Zomaya FedML 29 2 0 23 Sep 2024
Secure Federated Learning for Cognitive Radio Sensing Małgorzata Wasilewska H. Bogucka H. Vincent Poor 21 17 0 23 Mar 2023
RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-day Attacks in IoT Alberto Huertas Celdrán Pedro Miguel Sánchez Sánchez Jan von der Assen T. Schenk Gérome Bovet Gregorio Martínez Pérez Burkhard Stiller AAML 27 6 0 30 Dec 2022
Analyzing the Robustness of Decentralized Horizontal and Vertical Federated Learning Architectures in a Non-IID Scenario Pedro Miguel Sánchez Sánchez Alberto Huertas Celdrán Enrique Tomás Martínez Beltrán Daniel Demeter Gérome Bovet Gregorio Martínez Pérez Burkhard Stiller AAML FedML 21 6 0 20 Oct 2022