Tackling Heterogeneity in Medical Federated learning via Vision Transformers

no code implementations • 13 Oct 2023 • Erfan Darzi, Yiqing Shen, Yangming Ou, Nanna M. Sijtsema, P. M. A van Ooijen

Optimization-based regularization methods have been effective in addressing the challenges posed by data heterogeneity in medical federated learning, particularly in improving the performance of underrepresented clients.

Federated Learning

Fed-Safe: Securing Federated Learning in Healthcare Against Adversarial Attacks

no code implementations • 12 Oct 2023 • Erfan Darzi, Nanna M. Sijtsema, P. M. A van Ooijen

This paper explores the security aspects of federated learning applications in medical image analysis.

Federated Learning

A Comparative Study of Federated Learning Models for COVID-19 Detection

no code implementations • 28 Mar 2023 • Erfan Darzidehkalani, Nanna M. Sijtsema, P. M. A van Ooijen

Our results demonstrate good performance for detecting COVID-19 patients and might be useful in deploying FL algorithms for covid-19 detection and medical image analysis in general.

Federated Learning Privacy Preserving