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..
Towards Fair Graph Federated Learning via Incentive Mechanisms
Authors: Chenglu Pan, Jiarong Xu, Yue Yu, Ziqi Yang, Qingbiao Wu, Chunping Wang, Lei Chen, Yang Yang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our model achieves the best trade-off between accuracy and the fairness of model gradient, as well as superior payoff fairness. |
| Researcher Affiliation | Collaboration | 1Zhejiang University 2Fudan University 3ZJU-Hangzhou Global Scientific and Technological Innovation Center 4Fin Volution Group |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link confirming the release of open-source code for the described methodology. |
| Open Datasets | Yes | We use three graph classification datasets: PROTEINS, DD, and IMDB-BINARY. |
| Dataset Splits | No | The paper states: 'We retain 10% of all the graphs as the global test set for the server, and the remaining graphs are distributed to 10 agents. In each agent, we randomly split 90% for training and 10% for testing.' No explicit validation split information is provided for local agent data. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using a GIN network and Adam optimizer, and a motif extraction method from a cited paper, but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | We set the parameters β1 and β2 in Eq. (10) as 0.05 and 1, the parameter λ in Eq. (14) as 0.1, β in Eq. (4) as 1, and the budget B of payoff as 1... We utilized a three-layer GIN network with a hidden size of 64 and a dropout rate of 0.5... An Adam optimizer with a learning rate of 0.001 and weight decay of 5e 4 is employed. The communication round is 200, the epoch of local training on agents is 1 and the batch size is 128. |