reproducibilityindex.ai

FedBG: Proactively Mitigating Bias in Cross-Domain Graph Federated Learning Using Background Data

Authors: Sheng Huang, Lele Fu, Tianchi Liao, Bowen Deng, Chuanfu Zhang, Chuan Chen

Reproducibility Variable	Result	LLM Response
Research Type	Experimental	Extensive experiments on two real-world datasets demonstrate the sufficient motivation and effectiveness of the proposed method.
Researcher Affiliation	Academia	Sheng Huang , Lele Fu , Tianchi Liao , Bowen Deng , Chuanfu Zhang and Chuan Chen Sun Yat-sen University, Guangzhou, China EMAIL, EMAIL
Pseudocode	No	The paper states: "The complete algorithm is shown in Appendix." However, the provided text does not include an appendix with the algorithm or pseudocode.
Open Source Code	No	The paper does not contain an explicit statement about releasing source code, nor does it provide a link to a code repository.
Open Datasets	Yes	The evaluations are performed on two real-world datasets, including Twitch and Facebook100.
Dataset Splits	Yes	10% of the nodes of each domain s graph data in Facebook100 are sampled as the training data to make Facebook100-lite.
Hardware Specification	No	The paper does not provide any specific hardware details used for running the experiments.
Software Dependencies	No	The paper does not provide specific software names with version numbers.
Experiment Setup	No	The paper does not provide specific experimental setup details such as hyperparameter values, optimizer settings, or training configurations in the main text. It refers to an appendix for the complete algorithm, but the appendix is not provided.