Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
SAGDA: Achieving $\mathcal{O}(\epsilon^{-2})$ Communication Complexity in Federated Min-Max Learning
Authors: Haibo Yang, Zhuqing Liu, Xin Zhang, Jia Liu
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct numerical experiments using two machine learning problems (Logistic Regression and AUC Maximization) to verify our theoretical results for SAGDA as well as FSGDA. |
| Researcher Affiliation | Academia | Haibo Yang Dept. of ECE The Ohio State University Columbus, OH 43210 EMAIL; Zhuqing Liu Dept. of ECE The Ohio State University Columbus, OH 43210 EMAIL; Xin Zhang Dept. of Statistics Iowa State University Ames, IA 50010 EMAIL; Jia Liu Dept. of ECE The Ohio State University Columbus, OH 43210 EMAIL |
| Pseudocode | Yes | Algorithm 1 The Stochastic Averaging Gradient Descent Ascent (SAGDA) Algorithm.; Algorithm 2 Federated Stochastic Gradient Descent Ascent (FSGDA) Algorithm. |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] See detailed instructions in Section 4 and Appendix, but codes are private. |
| Open Datasets | Yes | We test the convergence performance of our algorithms using the a9a dataset [40] and MNIST [41] from LIBSVM repository. |
| Dataset Splits | No | The paper describes how data is distributed across clients for federated learning, and how a subset of the MNIST dataset was created, but not standard train/validation/test splits of the overall dataset used for evaluation. |
| Hardware Specification | No | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [No] |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers for reproducibility. |
| Experiment Setup | Yes | The learning rates are chosen as ηx,l = ηy,l = 10 2, ηx,g = ηy,g = 2, local updates K = 10. |