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 Graph Condensation with Information Bottleneck Principles
Authors: Bo Yan, Sihao He, Cheng Yang, Shang Liu, Yang Cao, Chuan Shi
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on five real-world datasets and show that Fed GC outperforms centralized GC and FGL methods, especially in large-scale datasets. Meanwhile, Fed GC can consistently protect membership privacy during the whole federated training process. |
| Researcher Affiliation | Academia | 1Beijing University of Posts and Telecommunications 2Institute of Science Tokyo 3China University of Mining and Technology EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods using mathematical formulations and prose, but no explicit pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper does not contain an explicit statement regarding the availability of source code, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Datasets. Follow (Jin et al. 2022b; Zheng et al. 2023), we evaluate Fed GC on five graph datasets on node classification task, including Cora, Citeseer, Ogbn-arxiv, Flickr, and Reddit. |
| Dataset Splits | Yes | We adopt the public splits provided in (Jin et al. 2022b). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types, or cloud instance specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | Yes | Following (Jin et al. 2022b), we report results under different condensation ratio r. Following (Yao et al. 2023), we test all FGL methods under the non-i.i.d. depicted by Dirichlet distribution (β=1). we set the client number n=10 for small datasets Cora and Citesser, and n=5 for large-scale datasets Ogbn-arxiv, Flickr, and Reddit. We run 5 times and report the average and variance of results. We utilize accuracy (Acc) to evaluate the condensation performance and AUC score to measure MIA performance. |