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 [1].
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. |