FedComm: Federated Learning as a Medium for Covert Communication

21 January 2022

Papers citing "FedComm: Federated Learning as a Medium for Covert Communication"

8 / 8 papers shown

Title
Adversarial Challenges in Network Intrusion Detection Systems: Research Insights and Future Prospects Sabrine Ennaji Fabio De Gaspari Dorjan Hitaj Alicia Kbidi Luigi V. Mancini AAML 32 0 0 27 Sep 2024
Have You Poisoned My Data? Defending Neural Networks against Data Poisoning Fabio De Gaspari Dorjan Hitaj Luigi V. Mancini AAML TDI 17 4 0 20 Mar 2024
DOLOS: A Novel Architecture for Moving Target Defense Giulio Pagnotta Fabio De Gaspari Dorjan Hitaj M. Andreolini M. Colajanni L. Mancini AAML 16 12 0 01 Mar 2023
TATTOOED: A Robust Deep Neural Network Watermarking Scheme based on Spread-Spectrum Channel Coding Giulio Pagnotta Dorjan Hitaj B. Hitaj F. Pérez-Cruz L. Mancini 14 5 0 12 Feb 2022
PassFlow: Guessing Passwords with Generative Flows Giulio Pagnotta Dorjan Hitaj Fabio De Gaspari L. Mancini 8 13 0 13 May 2021
Turning Federated Learning Systems Into Covert Channels Gabriele Costa Fabio Pinelli S. Soderi Gabriele Tolomei FedML 37 10 0 21 Apr 2021
A Style-Based Generator Architecture for Generative Adversarial Networks Tero Karras S. Laine Timo Aila 264 10,344 0 12 Dec 2018
Analyzing Federated Learning through an Adversarial Lens A. Bhagoji Supriyo Chakraborty Prateek Mittal S. Calo FedML 177 1,032 0 29 Nov 2018