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.
Personalized Subgraph Federated Learning
Authors: Jinheon Baek, Wonyong Jeong, Jiongdao Jin, Jaehong Yoon, Sung Ju Hwang
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 35,824 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,947 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 | We validate our FED-PUB for its subgraph FL performance on six datasets, considering both non-overlapping and overlapping subgraphs, on which it significantly outperforms relevant baselines. |
| Researcher Affiliation | Academia | 1KAIST. Correspondence to: Jinheon Baek, and Sung Ju Hwang <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 FED-PUB Client Algorithm |
| Open Source Code | Yes | Our code is available at https://github.com/JinheonBaek/FED-PUB. |
| Open Datasets | Yes | Specifically, we use six datasets: Cora, Cite Seer, Pubmed and ogbn-arxiv for citation graphs (Sen et al., 2008; Hu et al., 2020); Computer and Photo for product graphs (Mc Auley et al., 2015; Shchur et al., 2018). |
| Dataset Splits | Yes | For dataset splits, we randomly sample 20% nodes for training, 35% for validation, and 35% for testing, for all datasets except for the arxiv dataset. |
| Hardware Specification | Yes | We use two types of GPUs: Ge Force RTX 2080 Ti and TITAN XP, for training models. |
| Software Dependencies | No | The paper mentions 'Py Torch (Paszke et al., 2019) and Py Torch Geometric (Fey & Lenssen, 2019)' but does not specify their version numbers or any other software dependencies with version numbers. |
| Experiment Setup | Yes | Regarding hyperparameters, the number of hidden dimensions is set to 128, and the learning rate is set to 0.001. All models are optimized with Adam optimizer (Kingma & Ba, 2015). |