Serverless Federated AUPRC Optimization for Multi-Party Collaborative
  Imbalanced Data Mining
Xidong Wu Zhengmian Hu Jian Pei Heng Huang
Multi-party collaborative training, such as distributed learning and
federated learning, is used to address the big data challenges. However,
traditional multi-party collaborative training algorithms were mainly designed
for balanced data mining tasks and are intended to optimize accuracy
(\emph{e.g.}, cross-entropy). The data distribution in many real-world
applications is skewed and classifiers, which are trained to improve accuracy,
perform poorly when applied to imbalanced data tasks since models could be
significantly biased toward the primary class. Therefore, the Area Under
Precision-Recall Curve (AUPRC) was introduced as an effective metric. Although
single-machine AUPRC maximization methods have been designed, multi-party
collaborative algorithm has never been studied. The change from the
single-machine to the multi-party setting poses critical challenges.
  To address the above challenge, we study the serverless multi-party
collaborative AUPRC maximization problem since serverless multi-party
collaborative training can cut down the communications cost by avoiding the
server node bottleneck, and reformulate it as a conditional stochastic
optimization problem in a serverless multi-party collaborative learning setting
and propose a new ServerLess biAsed sTochastic gradiEnt (SLATE) algorithm to
directly optimize the AUPRC. After that, we use the variance reduction
technique and propose ServerLess biAsed sTochastic gradiEnt with Momentum-based
variance reduction (SLATE-M) algorithm to improve the convergence rate, which
matches the best theoretical convergence result reached by the single-machine
online method. To the best of our knowledge, this is the first work to solve
the multi-party collaborative AUPRC maximization problem.