Vertical Federated Graph Neural Network for Recommender System
Authors: Peihua Mai, Yan Pang
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical studies show that Ver Fed GNN has competitive prediction accuracy with existing privacy preserving GNN frameworks while enhanced privacy protection for users interaction information. |
| Researcher Affiliation | Academia | 1Department of Analytics and Operations, National University of Singapore, 119077 Singapore. |
| Pseudocode | Yes | Algorithm 1 Federated Vertical Graph Neural Network |
| Open Source Code | Yes | Source code: https://github.com/maiph123/Vertical GNN |
| Open Datasets | Yes | Dataset: We use two benchmark datasets for recommendation, Movie Lens-1M2 (ML-1M) and Book Crossing3. For Book Crossing we randomly select 6000 users and 3000 items. The items are divided into non-overlapping groups to simulate the vertical federated setting. 2https://grouplens.org/datasets/movielens/1m/ 3http://www2.informatik.uni-freiburg.de/ cziegler/BX/ |
| Dataset Splits | Yes | Cross-validation is adopted to tune the hyper-parameter, where the training-validation-testing ratio is 60%-20%-20%. |
| Hardware Specification | Yes | The experiment is implemented on Ubuntu Linux 20.04 server with 16-core CPU and 64GB RAM, where the programming language is Python. |
| Software Dependencies | No | The paper mentions the operating system and programming language: "The experiment is implemented on Ubuntu Linux 20.04 server...programming language is Python." However, it does not specify version numbers for any critical software libraries or frameworks (e.g., PyTorch, TensorFlow, scikit-learn) that would be needed for reproducibility. |
| Experiment Setup | Yes | Implementation and Hyper-parameter Setting: Appendix H details the implementation and hyperparameters. Based on the hyper-parameter optimization, we set embedding dimension D to 6, layer size K to 2, learning rate η to 0.05, and neighbor threshold thd to 4 for ML-1M and 8 for Book Crossing. We use sigmoid as the activation function. We consider privacy parameter r from 2 to 50, inverse dimension reduction ratio Nu/q from 1 to 100, and participation rate from 0.2 to 1. The immediate gradients are clipped within [ 0.5, 0.5] so that g1 g2 1 before applying ternary quantization. |