Hongyu Zhang, Dongyi Zheng, Lin Zhong, Xu Yang, Jiyuan Feng, Yunqing Feng, Qing Liao*
This is the source code and baselines of our ECML-PKDD'24 paper FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling. In this paper, we propose FedHCDR, a novel federated cross-domain recommendation framework with hypergraph signal decoupling.
Run the following command to install dependencies:
pip install -r requirements.txtNote that my Python version is 3.8.16.
We utilize publicly available datasets from the Amazon website to construct FedCDR scenarios. We select ten domains to generate three cross-domain scenarios: Food-Kitchen-Cloth-Beauty (FKCB), Sports-Clothing-Elec-Cell (SCEC), and Sports-Garden-Home-Toy (SGHT).
The preprocessed CDR datasets can be downloaded from Google Drive. You can download them and place them in the ./data path of this project.
FedHCDR
├── LICENSE LICENSE file
├── README.md README file
├── checkpoint Model checkpoints saving directory
│ └── ...
├── data Data directory
│ └── ...
├── log Log directory
│ └── ...
├── models Local model packages
│ ├── __init__.py Package initialization file
│ ├── dhcf dhcf package
│ │ ├── __init__.py Package initialization
│ │ ├── dhcf_model.py Model architecture
│ │ ├── config.py Model configuration file
│ │ └── modules.py Backbone modules (such as hyper GCN)
│ └── ...
├── pic Picture directory
│ └── FedHCDR-Framework.png Model framework diagram
├── utils Tools such as data reading, IO functions, training strategies, etc.
│ ├── __init__.py Package initialization file
│ ├── data_utils.py Data reading (including ratings and graphs)
│ ├── io_utils.py IO functions
│ └── train_utils.py Training strategies
├── client.py Client architecture
├── dataloader.py Customized dataloader
├── dataset.py Customized dataset
├── fl.py The overall process of federated learning
├── local_graph.py Local graph and hypergraph data structure
├── losses.py Loss functions
├── main.py Main function, including the complete data pipeline
├── requirements.txt Dependencies installation
├── server.py Server-side model parameters and user representations aggregation
├── trainer.py Training and test methods of FedHCDR and other baselines
└── .gitignore .gitignore fileTo train FedHCDR (ours), you can run the following command:
python -u main.py \
--num_round 60 \
--local_epoch 3 \
--eval_interval 1 \
--frac 1.0 \
--batch_size 1024 \
--log_dir log \
--method FedHCDR \
--lr 0.001 \
--seed 42 \
--lam 2.0 \
--gamma 2.0 \
Food Kitchen Clothing BeautyThere are a few points to note:
-
the positional arguments
Food Kitchen Clothing Beautyindicates training FedHCDR in FKCB scenario. If you want to choose another scenario, you can change it toSports Clothing Elec Cell(SCEC) orSports Garden Home Toys(SGHT). -
The argument
--lamis used to control local-global bi-directional knowledge transfer for FedHCDR method (ours). For FKCB,2.0is the best; for SCEC,3.0is the best; For SGHT,1.0is the best. -
The argument
--gammais used to control the intensity of hypergraph contrastive learning for FedHCDR method (ours). For FKCB,2.0is the best; for SCEC,1.0is the best; For SGHT,3.0is the best. -
If you restart training the model in a certain scenario, you can add the parameter
--load_prepto load the dataset preprocessed (including ratings and graphs) in the previous training to avoid repeated data preprocessing.
To test FedHCDR, you can run the following command:
python -u main.py \
--log_dir log \
--method FedHCDR \
--load_prep \
--model_id 1709476223 \
--do_eval \
--seed 42 \
Food Kitchen Clothing BeautyHere --model_id is the model ID under which you saved the model before. You can check the ID of the saved models in the checkpoint/domain_{$dataset} directory.
To train other baselines (LocalMF, LocalGNN, LocalDHCF, FedMF, FedGNN, PriCDR, FedP2FCDR, FedPPDM), you can run the following command:
python -u main.py \
--num_round 60 \
--local_epoch 3 \
--eval_interval 1 \
--frac 1.0 \
--batch_size 1024 \
--log_dir log \
--method FedPPDM \
--lr 0.001 \
--seed 42 \
Food Kitchen Clothing Beauty Here FedPPDM can be replaced with the name of the baselines you want to train.
For the local version without federated aggregation, you can run the following command:
python -u main.py \
--num_round 60 \
--local_epoch 3 \
--eval_interval 1 \
--frac 1.0 \
--batch_size 1024 \
--log_dir log \
--method LocalPPDM \
--lr 0.001 \
--seed 42 \
Food Kitchen Clothing Beauty Similarly, FedPPDM here can be replaced with the name of the baselines you want to train.
If you find this work useful for your research, please kindly cite FedHCDR by:
@misc{zhang2024fedhcdr,
title={FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling},
author={Hongyu Zhang and Dongyi Zheng and Lin Zhong and Xu Yang and Jiyuan Feng and Yunqing Feng and Qing Liao},
year={2024},
eprint={2403.02630},
archivePrefix={arXiv},
primaryClass={cs.LG}
}