  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. The proposed method is
implemented in our open-sourced library LibAUC (www.libauc.org) whose github
address is https://github.com/Optimization-AI/ICML2021_FedDeepAUC_CODASCA.
