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..
Federated Oriented Learning: A Practical One-Shot Personalized Federated Learning Framework
Authors: Guan Huang, Tao Shu
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on datasets Wildfire, Hurricane, CIFAR-10, CIFAR-100, and SVHN demonstrate that FOL consistently outperforms state-of-the-art one-shot Federated Learning (OFL) methods; for example, it achieves accuracy improvements of up to 39.24% over the baselines on the Wildfire dataset. Our experiment results verify that FOL consistently outperform counterparts, achieving accuracy improvements of up to 39.24% on Wildfire dataset. |
| Researcher Affiliation | Academia | 1Department of CSSE, Auburn University, Auburn, AL, 36849, USA. Correspondence to: Tao Shu <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Federated Oriented Learning (FOL) Algorithm 2 Top-K Model Selection |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | Datasets. We evaluate FOL s performance using four diverse datasets: Wildfire (Aaba, 2023), Hurricane (Park, 2021), CIFAR-10 (Krizhevsky, 2009), CIFAR-100 (Krizhevsky, 2009), and SVHN (Netzer et al., 2011). |
| Dataset Splits | Yes | Following the partitioning process, each client splits its local dataset into training, validation, and testing subsets in proportions of 70%, 15%, and 15%, respectively. |
| Hardware Specification | Yes | All models are built in Py Torch and trained/tested on two Ge Force RTX 4090 GPUs. |
| Software Dependencies | No | The paper mentions "All models are built in Py Torch" but does not specify a version number for PyTorch or any other software dependencies with version numbers. |
| Experiment Setup | Yes | For the Wildfire and Hurricane datasets, we use Stochastic Gradient Descent (SGD) with a momentum of 0.9, a weight decay of 0.001, a learning rate of 0.001, a batch size of 32, a patience of 20, and local training for 200 epochs. For CIFAR-10, CIFAR-100, and SVHN, we use SGD with a momentum of 0.9, a weight decay of 0.001, a learning rate of 0.01, a batch size of 128, a patience of 20, and local training for 300 epochs. In our experiments on CIFAR-10, CIFAR-100, SVHN, and the satellite datasets (Wildfire and Hurricane), we set λp = 0.1, γshared = 0.05, γunshared = 0.02 in Equation (5), and we set the distillation regularization weight in Equation (12) to 0.01. |