# Federated Compositional Deep AUC Maximization

arXiv: https://arxiv.org/abs/2304.10101 · HTML: https://mlsanity.com/paper/2304.10101/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 42 references that are in the map.
- First posted on arXiv 20 Apr 2023.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Local SGD Converges Fast and Communicates Little](https://mlsanity.com/paper/1805.09767/) (2018)
- [Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning](https://mlsanity.com/paper/1807.06629/) (2018)
- [On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems](https://mlsanity.com/paper/1906.00331/) (2019)
- [On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization](https://mlsanity.com/paper/1905.03817/) (2019)
- [A Patient-Centric Dataset of Images and Metadata for Identifying Melanomas Using Clinical Context](https://mlsanity.com/paper/2008.07360/) (2020)
- [Stochastic Compositional Gradient Descent: Algorithms for Minimizing Compositions of Expected-Value Functions](https://mlsanity.com/paper/1411.3803/) (2014)
- [Stochastic AUC Maximization with Deep Neural Networks](https://mlsanity.com/paper/1908.10831/) (2019)
- [FedNest: Federated Bilevel, Minimax, and Compositional Optimization](https://mlsanity.com/paper/2205.02215/) (2022)
- [A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems](https://mlsanity.com/paper/2010.15768/) (2020)
- [Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiency](https://mlsanity.com/paper/2102.13152/) (2021)
- [Federated Minimax Optimization: Improved Convergence Analyses and Algorithms](https://mlsanity.com/paper/2203.04850/) (2022)
- [Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks](https://mlsanity.com/paper/2005.02426/) (2020)
- [Single-Timescale Stochastic Nonconvex-Concave Optimization for Smooth Nonlinear TD Learning](https://mlsanity.com/paper/2008.10103/) (2020)
- [Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity](https://mlsanity.com/paper/2102.04635/) (2021)
- [Experiments on Parallel Training of Deep Neural Network using Model Averaging](https://mlsanity.com/paper/1507.01239/) (2015)
- [Compositional federated learning: Applications in distributionally robust averaging and meta learning](https://mlsanity.com/paper/2106.11264/) (2021)
- [FeDXL: Provable Federated Learning for Deep X-Risk Optimization](https://mlsanity.com/paper/2210.14396/) (2022)
- [Stochastic Recursive Variance Reduction for Efficient Smooth Non-Convex Compositional Optimization](https://mlsanity.com/paper/1912.13515/) (2019)

## Built on by (most cited first, 8 of 8)

- [Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining](https://mlsanity.com/paper/2308.03035/) (2023)
- [Stochastic Compositional Minimax Optimization with Provable Convergence Guarantees](https://mlsanity.com/paper/2408.12505/) (2024)
- [Stochastic Primal-Dual Double Block-Coordinate for Two-way Partial AUC Maximization](https://mlsanity.com/paper/2505.21944/) (2025)
- [Federated Multi-Sequence Stochastic Approximation with Local Hypergradient Estimation](https://mlsanity.com/paper/2306.01648/) (2023)
- [Achieving Linear Speedup in Decentralized Stochastic Compositional Minimax Optimization](https://mlsanity.com/paper/2307.13430/) (2023)
- [Online AUC Optimization Based on Second-order Surrogate Loss](https://mlsanity.com/paper/2510.21202/) (2025)
- [Semi-overlapping Multi-bandit Best Arm Identification for Sequential Support Network Learning](https://mlsanity.com/paper/2512.24959/) (2025)
- [Partial AUC Maximization from Positive-unlabeled Data](https://mlsanity.com/paper/2610.00284/) (2026)

## Abstract (arXiv)

Federated learning has attracted increasing attention due to the promise of balancing privacy and large-scale learning; numerous approaches have been proposed. However, most existing approaches focus on problems with balanced data, and prediction performance is far from satisfactory for many real-world applications where the number of samples in different classes is highly imbalanced. To address this challenging problem, we developed a novel federated learning method for imbalanced data by directly optimizing the area under curve (AUC) score. In particular, we formulate the AUC maximization problem as a federated compositional minimax optimization problem, develop a local stochastic compositional gradient descent ascent with momentum algorithm, and provide bounds on the computational and communication complexities of our algorithm. To the best of our knowledge, this is the first work to achieve such favorable theoretical results. Finally, extensive experimental results confirm the efficacy of our method.