mlsanityData to 7 Oct 2026

Federated Compositional Deep AUC Maximization

arXiv 2304.10101 ↗First posted 20 Apr 2023cs.LGcited by 8

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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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 25 of its 42 references that are in the map. First posted on arXiv 20 Apr 2023.

Builds on

25 of 42 references.

References · 25

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13 more
  1. 2014Stochastic Compositional Gradient Descent: Algorithms for Minimizing Compositions of Expected-Value Functionscited by 93
  2. 2019Stochastic AUC Maximization with Deep Neural Networkscited by 61
  3. 2022FedNest: Federated Bilevel, Minimax, and Compositional Optimizationcited by 60
  4. 2020A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problemscited by 57
  5. 2021Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiencycited by 40
  6. 2022Federated Minimax Optimization: Improved Convergence Analyses and Algorithmscited by 37
  7. 2020Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networkscited by 27
  8. 2020Single-Timescale Stochastic Nonconvex-Concave Optimization for Smooth Nonlinear TD Learningcited by 24
  9. 2021Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexitycited by 21
  10. 2015Experiments on Parallel Training of Deep Neural Network using Model Averagingcited by 14
  11. 2021Compositional federated learning: Applications in distributionally robust averaging and meta learningcited by 12
  12. 2022FeDXL: Provable Federated Learning for Deep X-Risk Optimizationcited by 10
  13. 2019Stochastic Recursive Variance Reduction for Efficient Smooth Non-Convex Compositional Optimizationcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 1; 2025: 3; 2026: 1.

8 papers.

  1. 2023Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Miningcited by 3
  2. 2024Stochastic Compositional Minimax Optimization with Provable Convergence Guaranteescited by 1
  3. 2025Stochastic Primal-Dual Double Block-Coordinate for Two-way Partial AUC Maximizationcited by 1
  4. 2023Federated Multi-Sequence Stochastic Approximation with Local Hypergradient Estimationcited by 0
  5. 2023Achieving Linear Speedup in Decentralized Stochastic Compositional Minimax Optimizationcited by 0
  6. 2025Online AUC Optimization Based on Second-order Surrogate Losscited by 0
  7. 2025Semi-overlapping Multi-bandit Best Arm Identification for Sequential Support Network Learningcited by 0
  8. 2026Partial AUC Maximization from Positive-unlabeled Datacited by 0