Solving a Class of Non-Convex Minimax Optimization in Federated Learning
Authors: Xidong Wu, Jianhui Sun, Zhengmian Hu, Aidong Zhang, Heng Huang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on fair classification and AUROC maximization show the efficiency of our algorithms. |
| Researcher Affiliation | Academia | Xidong Wu Electrical and Computer Engineering University of Pittsburgh Pittsburgh, PA 15213 xidong_wu@outlook.com; Jianhui Sun Computer Science University of Virginia Charlottesville, VA 22903 js9gu@virginia.edu; Zhengmian Hu Computer Science University of Maryland College Park, MD 20742 huzhengmian@gmail.com; Aidong Zhang Computer Science University of Virginia Charlottesville, VA 22903 aidong@virginia.edu; Heng Huang Computer Science University of Maryland College Park, MD 20742 henghuanghh@gmail.com |
| Pseudocode | Yes | Algorithm 1 Fed SGDA+ Algorithm; Algorithm 2 Fed SGDA-M Algorithm |
| Open Source Code | Yes | The code is available https://github.com/xidongwu/Federated-Minimax-and-Conditional-Stochastic-Optimization |
| Open Datasets | Yes | Fashion MNIST dataset has 70, 000 28 28 gray images (10 categories, 60, 000 training images and 10, 000 testing images). CIFAR-10 dataset consists of 50, 000 training images and 10, 000 testing images. Tiny-Image Net dataset has 200 classes of (64 64) colored images and each class has 500 training images, 50 validation images, and 50 test images. |
| Dataset Splits | Yes | Tiny-Image Net dataset has 200 classes of (64 64) colored images and each class has 500 training images, 50 validation images, and 50 test images. |
| Hardware Specification | Yes | Experiments are completed on the computer cluster with AMD EPYC 7513 Processors and NVIDIA RTX A6000. |
| Software Dependencies | No | The paper mentions using 'Py Torch' but does not specify version numbers for PyTorch or any other software dependencies, which would be necessary for reproducibility. |
| Experiment Setup | Yes | The network has 20 clients. In experiments, we run grid search for step size, and choose the step size for primal variable in the set {0.01, 0.03, 0.05, 0.1, 0.3} and that for dual variable in the set {0.001, 0.01, 0.1}. We choose the global step size in the set {0.1, 0.5, 1, 1.5, 2}. The batch-size b is in 50 and the inner loop number Q is seleted from {20, 50, 100}, The outer loop number S is selected from {1, 5, 10} for Fed SGDA+ and {1, 5, 10, Q} for local SGDA+. |