8 research outputs found
Fed-Safe: Securing Federated Learning in Healthcare Against Adversarial Attacks
This paper explores the security aspects of federated learning applications
in medical image analysis. Current robustness-oriented methods like adversarial
training, secure aggregation, and homomorphic encryption often risk privacy
compromises. The central aim is to defend the network against potential privacy
breaches while maintaining model robustness against adversarial manipulations.
We show that incorporating distributed noise, grounded in the privacy
guarantees in federated settings, enables the development of a adversarially
robust model that also meets federated privacy standards. We conducted
comprehensive evaluations across diverse attack scenarios, parameters, and use
cases in cancer imaging, concentrating on pathology, meningioma, and glioma.
The results reveal that the incorporation of distributed noise allows for the
attainment of security levels comparable to those of conventional adversarial
training while requiring fewer retraining samples to establish a robust model
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. This paper aims to
evaluate the vulnerabilities of federated learning networks in medical image
analysis against such attacks. Employing domain-specific MRI tumor and
pathology imaging datasets, we assess the effectiveness of known threat
scenarios in a federated learning environment. Our tests reveal that
domain-specific configurations can increase the attacker's success rate
significantly. The findings emphasize the urgent need for effective defense
mechanisms and suggest a critical re-evaluation of current security protocols
in federated medical image analysis systems
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. Our analysis uncovers a novel
exploitation avenue: using gradient information from prior global model
updates, adversaries can enhance the efficiency and transferability of their
attacks. Specifically, we demonstrate that single-step attacks (e.g. FGSM),
when aptly initialized, can outperform the efficiency of their iterative
counterparts but with reduced computational demand. Our findings underscore the
need to revisit our understanding of AI security in federated healthcare
settings
A comparative study of federated learning methods for COVID-19 detection
Deep learning has proven to be highly effective in diagnosing COVID-19; however, its efficacy is contingent upon the availability of extensive data for model training. The data sharing among hospitals, which is crucial for training robust models, is often restricted by privacy regulations. Federated learning (FL) emerges as a solution by enabling model training across multiple hospitals while preserving data privacy. However, the deployment of FL can be resource-intensive, necessitating efficient utilization of computational and network resources. In this study, we evaluate the performance and resource efficiency of five FL algorithms in the context of COVID-19 detection using Convolutional Neural Networks (CNNs) in a decentralized setting. The evaluation involves varying the number of participating entities, the number of federated rounds, and the selection algorithms. Our findings indicate that the Cyclic Weight Transfer algorithm exhibits superior performance, particularly when the number of participating hospitals is limited. These insights hold practical implications for the deployment of FL algorithms in COVID-19 detection and broader medical image analysis.</p