Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation

Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation.
Sichun Luo, Yuanzhang Xiao, Linqi Song.
CIKM2022

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This is the PyTorch implementation for the PerFedRec model. Another implementation and an improved version are available at PerFedRec++.

Introduction

In this paper, we propose a Graph Neural Network based Personalized Federated Recommendation (PerFedRec) framework via joint representation learning, user clustering, and model adaptation. Specifically, we construct a collaborative graph and incorporate attribute information to jointly learn the representation through a federated GNN. Based on these learned representations, we cluster users into different user groups and learn personalized models for each cluster. Then each user learns a personalized model by combining the global federated model, the cluster-level federated model, and the user's fine-tuned local model. To alleviate the heavy communication burden, we intelligently select a few representative users (instead of randomly picked users) from each cluster to participate in training.

Train

To train the model in centralized setting:

python Central.py

To train the model in federated setting:

python FedAvg.py

To train the model in the paper:

python PerFedRec.py

Citation

@inproceedings{luo2022personalized,
    title={Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation},
    author={Luo, Sichun and Xiao, Yuanzhang and Song, Linqi},
    booktitle={Proceedings of the 31st ACM International Conference on Information \& Knowledge Management},
    pages={4289--4293},
    year={2022}
}