The proliferation of deep learning applications in healthcare calls for data aggregation across various institutions, a practice often associated with significant privacy concerns. This concern intensifies in medical image analysis, where privacy-preserving mechanisms are paramount due to the data being sensitive in nature. Federated learning, which enables cooperative model training without direct data exchange, presents a promising solution. Nevertheless, the inherent vulnerabilities of federated learning necessitate further privacy safeguards. This study addresses this need by integrating differential privacy, a leading privacy-preserving technique, into a federated learning framework for medical image classification. We introduce a novel differentially private federated learning model and meticulously examine its impacts on privacy preservation and model performance. Our research confirms the existence of a trade-off between model accuracy and privacy settings. However, we demonstrate that strategic calibration of the privacy budget in differential privacy can uphold robust image classification performance while providing substantial privacy protection.

翻译：深度学习在医疗健康领域的广泛应用要求跨机构的数据聚合，这一实践常伴随显著的隐私担忧。在医学图像分析中，由于数据本质敏感，隐私保护机制至关重要，这加剧了此类担忧。联邦学习作为一种无需直接数据交换即可实现协作模型训练的方法，提供了有前景的解决方案。然而，联邦学习的固有脆弱性需要额外的隐私保障。本研究通过将差分隐私——一项领先的隐私保护技术——整合到用于医学图像分类的联邦学习框架中来应对这一需求。我们提出了一种新颖的差分隐私联邦学习模型，并细致考察了其对隐私保护和模型性能的影响。我们的研究证实了模型准确性与隐私设置之间存在权衡。然而，我们证明，通过战略性校准差分隐私中的隐私预算，可以在提供强隐私保护的同时，维持稳健的图像分类性能。