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..
FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning
Authors: Yinlin Zhu, Xunkai Li, Zhengyu Wu, Di Wu, Miao Hu, Rong-Hua Li
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on six public datasets consistently demonstrate the superiority of Fed TAD over state-of-the-art baselines. In this section, we conduct experiments to verify the effectiveness of Fed TAD. |
| Researcher Affiliation | Academia | 1Sun Yat-sen University, Guangzhou, China 2Beijing Institute of Technology, Beijing, China |
| Pseudocode | Yes | The complete algorithm of Fed TAD is presented in Algorithm 1. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We perform experiments on six widely used public benchmark datasets in graph learning: three small-scale citation network datasets (Cora, Cite Seer, Pub Med [Yang et al., 2016]), two medium-scale co-author datasets (CS, Physics [Shchur et al., 2018]), and one large-scale OGB dataset (ogbn-arxiv [Hu et al., 2020]). |
| Dataset Splits | No | The paper mentions 'We perform the hyperparameter search for Fed TAD using the Optuna framework [Akiba et al., 2019]' but does not specify the training, validation, and test splits (e.g., percentages or exact counts) for the datasets used in the main experiments, which are necessary for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'we perform the hyperparameter search for Fed TAD using the Optuna framework [Akiba et al., 2019]' but does not provide specific version numbers for Optuna or any other software dependencies crucial for replication. |
| Experiment Setup | Yes | The dimension of the hidden layer is set to 64 or 128. The local training epoch and round are set to 3 and 100, respectively. The learning rate of GNN is set to 1e-2, the weight decay is set to 5e-4, and the dropout is set to 0.5. Based on this, we perform the hyperparameter search for Fed TAD using the Optuna framework [Akiba et al., 2019] on λ1 and λ2 within {10 1, 10 2, 10 3}, and I, Ig, Id within {1, 3, 5, 10}. |