Federated Oriented Learning: A Practical One-Shot Personalized Federated Learning Framework
Authors: Guan Huang, Tao Shu
ICML 2025 | Venue PDF | Archive PDF | Plain Text | 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. |