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..
FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference
Authors: Zihan Tan, Guancheng Wan, Wenke Huang, Mang Ye
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform extensive experiments on cross-dataset and cross-domain settings to demonstrate the superiority of our framework. Furthermore, We perform extensive experiments on cross-dataset and cross-domain settings to demonstrate the superiority of our framework. |
| Researcher Affiliation | Academia | 1 National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, Hubei Key Laboratory of Multimedia and Network Communication Engineering, School of Computer Science, Wuhan University, Wuhan, China. 2 Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan, China |
| Pseudocode | No | The paper provides architectural diagrams (Figure 2) and mathematical formulations, but no explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/Oakley Tan/Fed SSP. |
| Open Datasets | Yes | Follow the settings in [61], we use 15 public graph classification datasets from four different domains, including Small Molecules (MUTAG, BZR, COX2, DHFR, PTC_MR, AIDS, NCI1), Bioinformatics (PROTEIN, OHSU, Peking_1), Social Networks(IMDB-BINARY, IMDBMULTI), and Computer Vision (Letter-low, Letter-high, Letter-med) [52]. |
| Dataset Splits | Yes | For each setting, every client holds its unique graph dataset, among which 10% are held out for testing, 10% for validation, and 80% for training. |
| Hardware Specification | Yes | The experiments are conducted using NVIDIA Ge Force RTX 3090 GPUs as the hardware platform, coupled with Intel(R) Xeon(R) Gold 6240 CPU @ 2.60GHz. |
| Software Dependencies | No | The paper mentions leveraging the Adam W optimizer but does not specify specific version numbers for software libraries (e.g., PyTorch, TensorFlow) or other key software components used for the experiments. |
| Experiment Setup | Yes | We leverage the Adam W optimizer [31] for local GNNs with learning rate 0.001, the default parameter of ϵ = 1e 8, and (β1, β2) = (0.99, 0.999), as suggested by [54, 85]. The number of communication rounds is 200 for all FL methods. For results in Tab. 1, we set up 4 heads for the multi-head attention mechanism while 128 for the hidden dimension. |