Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks

arXiv2005.02426MaRDI QIDQ6340109

Author name not available (Why is that?)

Publication date: 5 May 2020

Abstract: In this paper, we study distributed algorithms for large-scale AUC maximization with a deep neural network as a predictive model. Although distributed learning techniques have been investigated extensively in deep learning, they are not directly applicable to stochastic AUC maximization with deep neural networks due to its striking differences from standard loss minimization problems (e.g., cross-entropy). Towards addressing this challenge, we propose and analyze a communication-efficient distributed optimization algorithm based on a {it non-convex concave} reformulation of the AUC maximization, in which the communication of both the primal variable and the dual variable between each worker and the parameter server only occurs after multiple steps of gradient-based updates in each worker. Compared with the naive parallel version of an existing algorithm that computes stochastic gradients at individual machines and averages them for updating the model parameters, our algorithm requires a much less number of communication rounds and still achieves a linear speedup in theory. To the best of our knowledge, this is the extbf{first} work that solves the {it non-convex concave min-max} problem for AUC maximization with deep neural networks in a communication-efficient distributed manner while still maintaining the linear speedup property in theory. Our experiments on several benchmark datasets show the effectiveness of our algorithm and also confirm our theory.

Has companion code repository: https://github.com/ZhishuaiGuo/DistributedAUC

This page was built for publication: Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6340109)