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..
Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space
Authors: Mohsin Hasan, Guojun Zhang, Kaiyang Guo, Xi Chen, Pascal Poupart
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method is evaluated on a variety of regression and classification datasets to demonstrate its superiority in calibration to other baselines, even as data heterogeneity increases. |
| Researcher Affiliation | Collaboration | Mohsin Hasan1,2, Guojun Zhang3, Kaiyang Guo3, Xi Chen3, Pascal Poupart1,2 1University of Waterloo 2Vector Institute 3Huawei Noah s Ark Lab |
| Pseudocode | Yes | Algorithm 1: Distilled β-Pred Bayes |
| Open Source Code | Yes | Code available at https://github.com/hasanmohsin/beta Pred Bayes FL. |
| Open Datasets | Yes | The method was evaluated for classification on the following datasets: MNIST (Lecun et al. 1998), Fashion MNIST (Xiao, Rasul, and Vollgraf 2017), EMNIST (Cohen et al. 2017) (using a split with 62 classes), CIFAR10 and CIFAR100 (Krizhevsky, Hinton et al. 2009). ... The regression datasets used for evaluation include: the wine quality (Cortez et al. 2009), air quality (De Vito et al. 2008), forest fire (Cortez and Morais 2007), real estate (Yeh and Hsu 2018), and bike rental (Fanaee-T and Gama 2013) datasets from the UCI repository (Dua and Graff 2017). |
| Dataset Splits | Yes | All tests used 5 clients, with a distillation set composed of 20% of the original training set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., 'Python 3.8, PyTorch 1.9'). |
| Experiment Setup | No | Further experimental details, such as the models, and hyperparameters, are included in the appendix. |