This repository is the official implementation of the paper FedRKG: A Privacy-preserving Federated Recommendation Framework via Knowledge Graph Enhancement.
Abstract: Federated Learning (FL) has emerged as a promising approach for preserving data privacy in recommendation systems by training models locally. Recently, Graph Neural Networks (GNN) have gained popularity in recommendation tasks due to their ability to capture high-order interactions between users and items. However, privacy concerns prevent the global sharing of the entire user-item graph. To address this limitation, some methods create pseudo-interacted items or users in the graph to compensate for missing information for each client. Unfortunately, these methods introduce random noise and raise privacy concerns. In this paper, we propose FedRKG, a novel federated recommendation system, where a global knowledge graph (KG) is constructed and maintained on the server using publicly available item information, enabling higher-order user-item interactions. On the client side, a relation-aware GNN model leverages diverse KG relationships. To protect local interaction items and obscure gradients, we employ pseudo-labeling and Local Differential Privacy (LDP). Extensive experiments conducted on three real-world datasets demonstrate the competitive performance of our approach compared to centralized algorithms while ensuring privacy preservation. Moreover, FedRKG achieves an average accuracy improvement of 4% compared to existing federated learning baselines.
Our implementations heavily rely on Docker and the detailed environment setup refers to Dockerfile under
the ../environments folder.
By running command docker-compose build under the folder environments, you can build our main docker
image pytorch-mpi.
To train and evaluate the model(s) in the paper, run the following commands.
python run.py --arch kgcn --complex_arch master=kgcn_kg,worker=kgcn_aggregate --experiment serial --data music --pin_memory True --batch_size 32 --num_workers 1 --partition_data non_iid_dirichlet --non_iid_alpha 1 --train_data_ratio 1 --val_data_ratio 0 --n_clients 1872 --participation_ratio 1 --n_comm_rounds 2000 --local_n_epochs 1 --world_conf 0,0,1,1,100 --on_cuda True --fl_aggregate scheme=federated_average --optimizer adam --lr 5e-4 --local_prox_term 0 --lr_warmup False --lr_warmup_epochs 5 --lr_warmup_epochs_upper_bound 150 --lr_scheduler MultiStepLR --lr_decay 0.1 --weight_decay 1e-4 --use_nesterov False --momentum_factor 0 --track_time False --display_tracked_time False --hostfile hostfile --manual_seed 7 --pn_normalize True --same_seed_process False --python_path $HOME/anaconda/envs/pytorch2/bin/python