Decoupled Subgraph Federated Learning
Authors: Javad Aliakbari, Johan Östman, Alexandre Graell i Amat
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the effectiveness of FEDSTRUCT through experimental results conducted on six datasets for semi-supervised node classification, showcasing performance close to the centralized approach across various scenarios, including different data partitioning methods, varying levels of label availability, and number of clients. |
| Researcher Affiliation | Collaboration | Javad Aliakbari1 Johan Ostman2 Alexandre Graell i Amat1 1Chalmers University of Technology 2AI Sweden |
| Pseudocode | Yes | The FEDSTRUCT framework is illustrated in Figure 2 and described in Alg. 1 in App. B. (...) Algorithm 1 FEDSTRUCT (...) Algorithm 2 FEDSTRUCT using HOP2VEC (...) Algorithm 3 Private acquisition of A[i] |
| Open Source Code | Yes | The source code is publicly available in the Github Link. |
| Open Datasets | Yes | The datasets considered are: Cora (Sen et al., 2008), Citeseer (Sen et al., 2008), Pubmed (Namata et al., 2012), Chameleon (Pei et al., 2020), Amazon Photo (Shchur et al., 2018), and Amazon Ratings (Platonov et al., 2023). |
| Dataset Splits | Yes | We focus on a strongly semi-supervised setting where data is split into training, validation, and test sets containing 10%, 10%, and 80% of the nodes, respectively. |
| Hardware Specification | Yes | All the experiments are obtained using an Nvidia A30 with 24GB of memory. |
| Software Dependencies | No | The paper mentions using GNNs like GRAPHSAGE (Hamilton et al., 2017) but does not provide specific version numbers for any software dependencies (e.g., programming languages, libraries, frameworks). There is no explicit list of software or their versions. |
| Experiment Setup | Yes | In Table 5, we provide the step sizes λ and λs for the gradient descent step during the training, the weight decay in the L2 regularization, the number of training iterations (epochs), the number of layers L in the node feature embedding, the number of layers Ls in the DECOUPLED GCN, the dimensionality of the NSFs, ds, the pruning parameter p, and the model architecture of the node feature and node structure feature predictors, fθf and gθs, respectively. |