164 citations · 179 across the 4 of their papers we have counts for
4 papers
FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devices
Minxue Tang, Jianyi Zhang, Mingyuan Ma +5
Federated adversarial training can effectively complement adversarial robustness into the privacy-preserving federated learning systems. However, the high demand for memory capacit…
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective
Jingwei Sun, Ang Li, Louis DiValentin +3
Federated learning (FL) is a popular distributed learning framework that trains a global model through iterative communications between a central server and edge devices. Recent wo…
Security and Privacy Considerations for Machine Learning Models Deployed in the Government and Public Sector (white paper)
Nader Sehatbakhsh, Ellie Daw, Onur Savas +2
As machine learning becomes a more mainstream technology, the objective for governments and public sectors is to harness the power of machine learning to advance their mission by r…
A Review of Cybersecurity Incidents in the Water Sector
Amin Hassanzadeh, Amin Rasekh, Stefano Galelli +4
This study presents a critical review of disclosed, documented, and malicious cybersecurity incidents in the water sector to inform safeguarding efforts against cybersecurity threa…