# Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks

arXiv: https://arxiv.org/abs/2005.02426 · HTML: https://mlsanity.com/paper/2005.02426/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2020.

## Built on by (most cited first, 27 of 27)

- [AUC Maximization in the Era of Big Data and AI: A Survey](https://mlsanity.com/paper/2203.15046/) (2022)
- [Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity](https://mlsanity.com/paper/2112.05604/) (2021)
- [The Complexity of Nonconvex-Strongly-Concave Minimax Optimization](https://mlsanity.com/paper/2103.15888/) (2021)
- [Topology-aware Generalization of Decentralized SGD](https://mlsanity.com/paper/2206.12680/) (2022)
- [Federated Minimax Optimization: Improved Convergence Analyses and Algorithms](https://mlsanity.com/paper/2203.04850/) (2022)
- [Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence](https://mlsanity.com/paper/2104.08736/) (2021)
- [Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity](https://mlsanity.com/paper/2102.04635/) (2021)
- [Momentum Accelerates the Convergence of Stochastic AUPRC Maximization](https://mlsanity.com/paper/2107.01173/) (2021)
- [FeDXL: Provable Federated Learning for Deep X-Risk Optimization](https://mlsanity.com/paper/2210.14396/) (2022)
- [Decentralized Stochastic Gradient Descent Ascent for Finite-Sum Minimax Problems](https://mlsanity.com/paper/2212.02724/) (2022)
- [Solving a Class of Non-Convex Minimax Optimization in Federated Learning](https://mlsanity.com/paper/2310.03613/) (2023)
- [Federated Compositional Deep AUC Maximization](https://mlsanity.com/paper/2304.10101/) (2023)
- [Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities](https://mlsanity.com/paper/2111.02400/) (2021)
- [Enhanced Adaptive Gradient Algorithms for Nonconvex-PL Minimax Optimization](https://mlsanity.com/paper/2303.03984/) (2023)
- [Asymptotically Unbiased Instance-wise Regularized Partial AUC Optimization: Theory and Algorithm](https://mlsanity.com/paper/2210.03967/) (2022)
- [Faster Stochastic Algorithms for Minimax Optimization under Polyak--Lojasiewicz Conditions](https://mlsanity.com/paper/2307.15868/) (2023)
- [Enabling All In-Edge Deep Learning: A Literature Review](https://mlsanity.com/paper/2204.03326/) (2022)
- [Federated Conditional Stochastic Optimization](https://mlsanity.com/paper/2310.02524/) (2023)
- [Provable Multi-instance Deep AUC Maximization with Stochastic Pooling](https://mlsanity.com/paper/2305.08040/) (2023)
- [Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining](https://mlsanity.com/paper/2308.03035/) (2023)
- [CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problems](https://mlsanity.com/paper/2105.14216/) (2021)
- [Local AdaGrad-Type Algorithm for Stochastic Convex-Concave Optimization](https://mlsanity.com/paper/2106.10022/) (2021)
- [Federated Stochastic Minimax Optimization under Heavy-Tailed Noises](https://mlsanity.com/paper/2511.04456/) (2025)
- [SAGDA: Achieving O(ε-2) Communication Complexity in Federated Min-Max Learning](https://mlsanity.com/paper/2210.00611/) (2022)
- [Closing the Approximation Gap of Partial AUC Optimization: A Tale of Two Formulations](https://mlsanity.com/paper/2512.01213/) (2025)
- [Communication-Efficient Federated AUC Maximization with Cyclic Client Participation](https://mlsanity.com/paper/2601.01649/) (2026)
- [Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks](https://mlsanity.com/paper/2605.26243/) (2026)

## Abstract (arXiv)

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 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.