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..
Communication-Efficient Federated Group Distributionally Robust Optimization
Authors: Zhishuai Guo, Tianbao Yang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The effectiveness of our algorithms has been demonstrated on a variety of real-world tasks, including natural language processing and computer vision. |
| Researcher Affiliation | Academia | Zhishuai Guo, Tianbao Yang Department of Computer Science and Engineering Texas A&M University EMAIL,EMAIL |
| Pseudocode | Yes | Algorithm 1 FGDRO-CVa R |
| Open Source Code | No | All used data are publicly available. Code will be released later. |
| Open Datasets | Yes | We use Pile [15], Civil Comments [5], Camelyon17 [1], i Wild Cam2020 [3], and Poverty [74]. |
| Dataset Splits | Yes | Camelyon17 focuses on tumor detection from lymph node images [1], with data from five hospitals split into training (3), validation (1), and testing (1) sets. |
| Hardware Specification | Yes | Each algorithm was run on a high performance cluster where each machine uses a NVIDIA A100 GPU. |
| Software Dependencies | No | The paper mentions specific models and frameworks (e.g., GPT2, Distil BERT, Huggingface, DenseNet, ResNet) but does not provide specific version numbers for programming languages, libraries, or other software dependencies. |
| Experiment Setup | Yes | We tune the initial step size in [1e-4, 1e-3, 1e-2, 1e-1]. All algorithms set the communication interval I = 32 unless otherwise specified. The local mini-batch sizes are set to 32. Experiments are run for 20K local iterations except for Pile, which runs for 200K iterations. The β parameters of FGDRO-KL and FGDRO-KL-Adam are tuned in [0.01, 0.1, 0.2, 0.5]. |