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.
Code (0)
등록된 구현이 없습니다.
Tasks
Federated LearningMedical Image AnalysisPrivacy PreservingSimilar Papers 제목 키워드 기반
MedFedPure: A Medical Federated Framework with MAE-based Detection and Diffusion Purification for Inference-Time Attacks
Artificial intelligence (AI) has shown great potential in medical imaging, particularly for brain tumor detection using Magnetic Resonance Imaging (MRI). However, the models remain vulnerable at inference time when they …
Personalized Federated LearningAdversarial RobustnessFed-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 r…
Federated LearningMedical Image AnalysisThe 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 up…
Federated LearningMedical Image AnalysisDefending against adversarial attacks on medical imaging AI system, classification or detection?
Medical imaging AI systems such as disease classification and segmentation are increasingly inspired and transformed from computer vision based AI systems. Although an array of adversarial training and/or loss function b…
Adversarial DefenseGeneral ClassificationCan collaborative learning be private, robust and scalable?
In federated learning for medical image analysis, the safety of the learning protocol is paramount. Such settings can often be compromised by adversaries that target either the private data used by the federation or the …
Adversarial RobustnessFederated LearningMedical Image AnalysisModel Compression