GNN4FR: A Lossless GNN-based Federated Recommendation Framework

2023-07-25 · Guowei Wu, Weike Pan, Zhong Ming

Graph neural networks (GNNs) have gained wide popularity in recommender systems due to their capability to capture higher-order structure information among the nodes of users and items. However, these methods need to collect personal interaction data between a user and the corresponding items and then model them in a central server, which would break the privacy laws such as GDPR. So far, no existing work can construct a global graph without leaking each user's private interaction data (i.e., his or her subgraph). In this paper, we are the first to design a novel lossless federated recommendation framework based on GNN, which achieves full-graph training with complete high-order structure information, enabling the training process to be equivalent to the corresponding un-federated counterpart. In addition, we use LightGCN to instantiate an example of our framework and show its equivalence.

📄 PDF Abstract BibTeX arXiv:2308.01197

Code (0)

등록된 구현이 없습니다.

Tasks

Recommendation Systems

Methods 이 논문이 사용한 방법론

LightGCN LightGCN is a type of graph convolutional neural network (GCN), including only the most essential component in GCN (neighborhood…

Lossless and Privacy-Preserving Graph Convolution Network for Federated Item Recommendation

2024-12-02 · Guowei Wu, Weike Pan, Qiang Yang, Zhong Ming

Graph neural network (GNN) has emerged as a state-of-the-art solution for item recommendation. However, existing GNN-based recommendation methods rely on a centralized storage of fragmented user-item interaction sub-grap…

Graph Neural Network Privacy Preserving

SecureBoost: A Lossless Federated Learning Framework

2019-01-25 · Kewei Cheng, Tao Fan, Yilun Jin, Yang Liu 외

The protection of user privacy is an important concern in machine learning, as evidenced by the rolling out of the General Data Protection Regulation (GDPR) in the European Union (EU) in May 2018. The GDPR is designed to…

BIG-bench Machine Learning Entity Alignment Federated Learning Privacy Preserving

A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations

2020-08-25 · Mingkai Huang, Hao Li, Bing Bai, Chang Wang 외

Privacy-preserving recommendations are recently gaining momentum, since the decentralized user data is increasingly harder to collect, by recommendation service providers, due to the serious concerns over user privacy an…

Collaborative Filtering Federated Learning Privacy Preserving

Cali3F: Calibrated Fast Fair Federated Recommendation System

2022-05-26 · Zhitao Zhu, Shijing Si, Jianzong Wang, Jing Xiao

The increasingly stringent regulations on privacy protection have sparked interest in federated learning. As a distributed machine learning framework, it bridges isolated data islands by training a global model over devi…

Fairness Federated Learning Privacy Preserving Recommendation Systems

FedXGBoost: Privacy-Preserving XGBoost for Federated Learning

2021-06-20 · Nhan Khanh Le, Yang Liu, Quang Minh Nguyen, Qingchen Liu 외

Federated learning is the distributed machine learning framework that enables collaborative training across multiple parties while ensuring data privacy. Practical adaptation of XGBoost, the state-of-the-art tree boostin…

Federated Learning Privacy Preserving