Deep AUC (area under the ROC curve) Maximization (DAM) has attracted much attention recently due to its great potential for imbalanced data classification. However, the research on Federated Deep AUC Maximization (FDAM) is still limited. Compared with standard federated learning (FL) approaches that focus on decomposable minimization objectives, FDAM is more complicated due to its minimization objective is non-decomposable over individual examples. In this paper, we propose improved FDAM algorithms for heterogeneous data by solving the popular non-convex strongly-concave min-max formulation of DAM in a distributed fashion, which can also be applied to a class of non-convex strongly-concave min-max problems. A striking result of this paper is that the communication complexity of the proposed algorithm is a constant independent of the number of machines and also independent of the accuracy level, which improves an existing result by orders of magnitude. The experiments have demonstrated the effectiveness of our FDAM algorithm on benchmark datasets, and on medical chest X-ray images from different organizations. Our experiment shows that the performance of FDAM using data from multiple hospitals can improve the AUC score on testing data from a single hospital for detecting life-threatening diseases based on chest radiographs.
翻译:最近,由于数据分类极有可能出现不平衡现象,深入ACU(ROC曲线下的地区)的最大化(DAM)最近引起了许多关注。然而,关于Federation Deep AUC(FDAM)最大化(FDAM)的研究仍然有限。与侧重于分解最小化目标的标准联合学习(FL)方法相比,FDCM(FL)由于其最小化目标而变得更加复杂,与个别例子相比是无法分解的。在本文件中,我们建议改进FDAM(FAM)的混杂数据的算法,方法是以分布式解决流行的非凝固度强的DAM微积分成型。DAM的配制也可以适用于非civex混凝固微积成型小口径问题。本文的一个引人注目的结果是,拟议的算法的通信复杂性与机器数量不相干,也与精确度不相干,后者通过数量级等提高现有结果。实验表明,我们FDAMM(FDAM)在基准数据集和不同组织的医疗胸X射线图像方面的算法是有效的。我们的实验表明,使用多家医院用于检测威胁性病的医院的数据,可以改进以测试。