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 | Conference PDF | Archive PDF | Plain Text | 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. |