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.

该研究探讨了医学图像分析中联邦学习应用的安全方面。通过引入联邦环境中的分布式噪音，可以开发出具备对抗攻击鲁棒性并符合联邦隐私标准的模型。研究结果表明，引入分布式噪音可达到与传统对抗训练相当的安全水平，同时需要更少的样本进行重新训练。