论文标题
对临床基准数据联合学习的可靠性和绩效评估
Reliability and Performance Assessment of Federated Learning on Clinical Benchmark Data
论文作者
论文摘要
由于已经在临床背景下应用了深度学习，因此由于收集和处理大量个人数据，隐私问题增加了。最近，建议联邦学习（FL）保护个人隐私，因为它在培训阶段不集中数据。在这项研究中，我们评估了包括MNIST和MIMIC-III在内的基准数据集上FL的可靠性和性能。此外，我们尝试验证模拟现实临床数据分布的数据集上的FL。我们实施了使用客户端和服务器体系结构的FL，并在修改后的MNIST和MIMIC-III数据集上测试了客户端和服务器FL。 FL在不平衡和极度偏斜的分布（即患者人数差异和每个医院的患者特征差异）上均提供了可靠的性能。因此，FL可以适用于将隐私应用于医疗数据。
As deep learning have been applied in a clinical context, privacy concerns have increased because of the collection and processing of a large amount of personal data. Recently, federated learning (FL) has been suggested to protect personal privacy because it does not centralize data during the training phase. In this study, we assessed the reliability and performance of FL on benchmark datasets including MNIST and MIMIC-III. In addition, we attempted to verify FL on datasets that simulated a realistic clinical data distribution. We implemented FL that uses a client and server architecture and tested client and server FL on modified MNIST and MIMIC-III datasets. FL delivered reliable performance on both imbalanced and extremely skewed distributions (i.e., the difference of the number of patients and the characteristics of patients in each hospital). Therefore, FL can be suitable to protect privacy when applied to medical data.
