3 citations · 7 across the 4 of their papers we have counts for
4 papers
The Hidden Adversarial Vulnerabilities of Medical Federated Learning
Erfan Darzi, Florian Dubost, Nanna. M. Sijtsema +1
In this paper, we delve into the susceptibility of federated medical image analysis systems to adversarial attacks. Our analysis uncovers a novel exploitation avenue: using gradien…
Fed-Safe: Securing Federated Learning in Healthcare Against Adversarial Attacks
Erfan Darzi, Nanna M. Sijtsema, P. M. A van Ooijen
This paper explores the security aspects of federated learning applications in medical image analysis. Current robustness-oriented methods like adversarial training, secure aggrega…
Exploring adversarial attacks in federated learning for medical imaging
Erfan Darzi, Florian Dubost, N. M. Sijtsema +1
Federated learning offers a privacy-preserving framework for medical image analysis but exposes the system to adversarial attacks. This paper aims to evaluate the vulnerabilities o…
A Comparative Study of Federated Learning Models for COVID-19 Detection
Erfan Darzidehkalani, Nanna M. Sijtsema, P. M. A van Ooijen
Deep learning is effective in diagnosing COVID-19 and requires a large amount of data to be effectively trained. Due to data and privacy regulations, hospitals generally have no ac…