  \underline{D}eep \underline{A}UC (area under the ROC curve)
\underline{M}aximization (DAM) has attracted much attention recently due to its
great potential for imbalanced data classification. However, the research on
\underline{F}ederated \underline{D}eep \underline{A}UC \underline{M}aximization
(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. 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. Of independent
interest, the proposed algorithm can also be applied to a class of
non-convex-strongly-concave min-max problems. 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.
