@inproceedings{
mao2024clusterdriven,
title={Cluster-driven Personalized Federated Recommendation with Interest-aware Graph Convolution Network for Multimedia},
author={Xingyuan Mao and Yuwen Liu and Lianyong Qi and Li Duan and Xiaolong Xu and Xuyun Zhang and Wanchun Dou and Amin Beheshti and Xiaokang Zhou},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=daxLPN9GUT}
}
Federated learning addresses privacy concerns in multimedia recommender systems by enabling collaborative model training without exchanging raw data. However, existing federated recommendation models are mainly based on basic backbones like Matrix Factorization (MF), which are inadequate to capture complex implicit interactions between users and multimedia content. Graph Convolutional Networks (GCNs) offer a promising method by utilizing the information from high-order neighbors, but face challenges in federated settings due to problems such as over-smoothing, data heterogeneity, and elevated communication expenses. To resolve these problems, we propose a Cluster-driven Personalized Federated Recommender System with Interest-aware Graph Convolution Network (CPF-GCN) for multimedia recommendation. CPF-GCN comprises a local interest-aware GCN module that optimizes node representations through subgraph-enhanced adaptive graph convolution operations, mitigating the over-smoothing problem by adaptively extracting information from layers and selectively utilizing high-order connectivity based on user interests. Simultaneously, a cluster-driven aggregation approach at the server significantly reduces communication costs by selectively aggregating models from clusters. The aggregation produces a global model and cluster-level models, combining them with the user's local model allows us to tailor the recommendation model for the user, achieving personalized recommendations. Moreover, we propose an adversarial optimization technique to further augment the robustness of CPF-GCN. Experiments on three datasets demonstrate that CPF-GCN significantly outperforms the state-of-the-art models.
Cluster-driven Personalized Federated Recommendation with Interest-aware Graph Convolution Network for Multimedia
Xingyuan Mao Yuwen Liu Lianyong Qi Li Duan Xiaolong Xu Xuyun Zhang Wanchun Dou Amin Beheshti Xiaokang Zhou