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

TL;DR

This paper introduces a federated recommendation framework using graph neural networks that learns personalized models for user groups, balancing privacy, heterogeneity, and communication efficiency, resulting in improved recommendation accuracy.

Contribution

The paper proposes a novel federated GNN framework with user clustering and model adaptation for personalized recommendations, reducing communication costs and enhancing performance.

Findings

Achieves superior recommendation accuracy over existing methods.

Effectively balances personalization and privacy in federated settings.

Reduces communication overhead by selecting representative users.

Abstract

Federated recommendation applies federated learning techniques in recommendation systems to help protect user privacy by exchanging models instead of raw user data between user devices and the central server. Due to the heterogeneity in user's attributes and local data, attaining personalized models is critical to help improve the federated recommendation performance. 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. Experiments on real-world datasets show that our proposed method achieves superior performance over existing methods.