Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Towards Addressing Label Skews in One-Shot Federated Learning
Authors: Yiqun Diao, Qinbin Li, Bingsheng He
ICLR 2023 | Venue PDF | LLM Run Details | Input Tokens: 33,767 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,920 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments show that Fed OV can significantly improve the test accuracy compared to state-of-the-art approaches in various label skew settings. |
| Researcher Affiliation | Academia | Yiqun Diao, Qinbin Li & Bingsheng He National University of Singapore EMAIL |
| Pseudocode | Yes | Algorithm 1: The Fed OV algorithm. |
| Open Source Code | Yes | Code is available at https://github.com/Xtra-Computing/Fed OV. |
| Open Datasets | Yes | Datasets We conduct experiments on MNIST, Fashion-MNIST, CIFAR-10 and SVHN datasets. We use the data partitioning methods in Li et al. (2021b) to simulate different label skews. |
| Dataset Splits | Yes | In each task, we use a half of the test dataset as the public dataset for distillation for Fed KT and Fed DF and the remaining for testing. |
| Hardware Specification | Yes | All experiments are conducted on a single 3090 GPU. |
| Software Dependencies | No | The paper mentions 'Adam optimizer' and 'Re Lu as the activation function' but does not provide specific version numbers for any software dependencies like programming languages or libraries. |
| Experiment Setup | Yes | For local training, we run 200 local epochs for each client. We set batch size to 64 and learning rate to 0.001. For PROSER, we choose β = 0.01, γ = 1, according to the default trade-off parameter setting in the official code1. For adversarial learning, we set 5 local steps and each step size 0.002. |