FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation

TL;DR

This paper introduces FedGNN, a federated learning framework for graph neural network-based recommendation systems that preserves user privacy through local training, differential privacy, and graph expansion techniques, achieving competitive accuracy.

Contribution

The paper presents a novel federated GNN framework that enables decentralized training with privacy protections, including gradient privacy and user-item interaction anonymity.

Findings

Achieves comparable recommendation accuracy to centralized methods.

Effectively protects user privacy with differential privacy and item anonymity.

Demonstrates scalability and robustness on six benchmark datasets.

Abstract

Graph neural network (GNN) is widely used for recommendation to model high-order interactions between users and items. Existing GNN-based recommendation methods rely on centralized storage of user-item graphs and centralized model learning. However, user data is privacy-sensitive, and the centralized storage of user-item graphs may arouse privacy concerns and risk. In this paper, we propose a federated framework for privacy-preserving GNN-based recommendation, which can collectively train GNN models from decentralized user data and meanwhile exploit high-order user-item interaction information with privacy well protected. In our method, we locally train GNN model in each user client based on the user-item graph inferred from the local user-item interaction data. Each client uploads the local gradients of GNN to a server for aggregation, which are further sent to user clients for updating local GNN models. Since local gradients may contain private information, we apply local differential privacy techniques to the local gradients to protect user privacy. In addition, in order to protect the items that users have interactions with, we propose to incorporate randomly sampled items as pseudo interacted items for anonymity. To incorporate high-order user-item interactions, we propose a user-item graph expansion method that can find neighboring users with co-interacted items and exchange their embeddings for expanding the local user-item graphs in a privacy-preserving way. Extensive experiments on six benchmark datasets validate that our approach can achieve competitive results with existing centralized GNN-based recommendation methods and meanwhile effectively protect user privacy.