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.

翻译：联邦学习为医学图像分析提供了一种隐私保护框架，但同时也使系统面临对抗性攻击的风险。本文旨在评估联邦学习网络在医学图像分析中针对此类攻击的脆弱性。通过使用领域特定的MRI肿瘤和病理影像数据集，我们评估了已知威胁场景在联邦学习环境中的有效性。实验结果表明，领域特定的配置可能显著提高攻击者的成功率。这些发现强调了有效防御机制的迫切需求，并建议对当前联邦医学图像分析系统的安全协议进行关键性的重新评估。