4.2k citations · 4.2k across the 8 of their papers we have counts for
5 papers · 1 filter
Tackling Heterogeneity in Medical Federated learning via Vision Transformers
Erfan Darzi, Yiqing Shen, Yangming Ou +2
Optimization-based regularization methods have been effective in addressing the challenges posed by data heterogeneity in medical federated learning, particularly in improving the…
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…