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The Hidden Adversarial Vulnerabilities of Medical Federated Learning
In this paper, we delve into the susceptibility of federated medical image analysis systems to adversarial attacks.
Tackling Heterogeneity in Medical Federated learning via Vision Transformers
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.
Fed-Safe: Securing Federated Learning in Healthcare Against Adversarial Attacks
This paper explores the security aspects of federated learning applications in medical image analysis.
Exploring adversarial attacks in federated learning for medical imaging
Federated learning offers a privacy-preserving framework for medical image analysis but exposes the system to adversarial attacks.
A Comparative Study of Federated Learning Models for COVID-19 Detection
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.
AI Technical Considerations: Data Storage, Cloud usage and AI Pipeline
Artificial intelligence (AI), especially deep learning, requires vast amounts of data for training, testing, and validation.
