PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training

This is the PyTorch implementation for PerFedRec++ model, an improved version of PerFedRec.

PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training.
Sichun Luo, Yuanzhang Xiao, Xinyi Zhang, Yang Liu, Wenbo Ding, Linqi Song.
ACM Transactions on Intelligent Systems and Technology (TIST) 2024

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Introduction

In this paper, we propose PerFedRec++, to enhance the personalized federated recommendation with self-supervised pre-training. Specifically, we utilize the privacy-preserving mechanism of federated recommender systems to generate two augmented graph views, which are used as contrastive tasks in self-supervised graph learning to pre-train the model. Pre-training enhances the performance of federated models by improving the uniformity of representation learning. Also, by providing a better initial state for federated training, pre-training makes the overall training converge faster, thus alleviating the heavy communication burden. We then construct a collaborative graph to learn the client representation through a federated graph neural network. Based on these learned representations, we cluster users into different user groups and learn personalized models for each cluster. Each user learns a personalized model by combining the global federated model, the cluster-level federated model, and its own fine-tuned local model.

Citation

If you find PerFedRec++ useful in your research or applications, please kindly cite:

@article{luo2023perfedrec++,
title={PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training},
author={Luo, Sichun and Xiao, Yuanzhang and Zhang, Xinyi and Liu, Yang and Ding, Wenbo and Song, Linqi},
journal={arXiv preprint arXiv:2305.06622},
year={2023}
}

Thanks for your interest in our work!

Acknowledgement

Thanks for SELFRec.