Federated Recommendation

Federated recommendation aims to build personalized recommendation systems while preserving user privacy by performing model training on decentralized data. Current research focuses on enhancing privacy through techniques like differential privacy and blockchain-based traceability, addressing data sparsity and heterogeneity with methods such as hybrid retrieval augmented generation and knowledge graph enhancement, and improving efficiency via low-rank training and update prediction. This field is significant for enabling collaborative recommendation across multiple data sources while adhering to privacy regulations, impacting both the development of more ethical AI systems and the practical deployment of personalized services.

Papers

November 3, 2024

October 16, 2024

P4GCN: Vertical Federated Social Recommendation with Privacy-Preserving Two-Party Graph Convolution Networks
Zheng Wang, Wanwan Wang, Yimin Huang, Zhaopeng Peng, Ziqi Yang, Cheng Wang, Xiaoliang Fan
Federated Recommendation Social Recommendation

October 11, 2024

Personalized Item Representations in Federated Multimodal Recommendation
Zhiwei Li, Guodong Long, Jing Jiang, Chengqi Zhang
Multimodal Data Recommendation System Federated Recommendation Item Embeddings Multimodal Recommendation Item Relevance

August 16, 2024

Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach
Zhiwei Li, Guodong Long, Tianyi Zhou, Jing Jiang, Chengqi Zhang
Collaborative Filtering Federated Recommendation FedCF Method

June 7, 2024

Marking the Pace: A Blockchain-Enhanced Privacy-Traceable Strategy for Federated Recommender Systems
Zhen Cai, Tao Tang, Shuo Yu, Yunpeng Xiao, Feng Xia
Local Differential Privacy Data Privacy Data Sharing Pace Adaptive Federated Recommendation Copyright Traceability

May 12, 2024

Navigating the Future of Federated Recommendation Systems with Foundation Models
Zhiwei Li, Guodong Long
Self Supervised Learning Transfer Learning Foundation Model Recommendation System Future Reasoning Federated Recommendation

March 7, 2024

Federated Recommendation via Hybrid Retrieval Augmented Generation
Huimin Zeng, Zhenrui Yue, Qian Jiang, Dong Wang
Retrieval Augmented Generation Recommendation Performance Federated Recommendation LLM Based Recommender System Hybrid Retrieval

February 18, 2024

Poisoning Federated Recommender Systems with Fake Users
Ming Yin, Yichang Xu, Minghong Fang, Neil Zhenqiang Gong
Federated Learning Poisoning Attack Malicious Agent Federated Recommendation Fake User Client Side

January 20, 2024

FedRKG: A Privacy-preserving Federated Recommendation Framework via Knowledge Graph Enhancement
Dezhong Yao, Tongtong Liu, Qi Cao, Hai Jin
Graph Neural Network Knowledge Graph Recommendation System Local Differential Privacy Federated Recommendation

January 8, 2024

Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Training
Ngoc-Hieu Nguyen, Tuan-Anh Nguyen, Tuan Nguyen, Vu Tien Hoang, Dung D. Le, Kok-Seng Wong
Secure Aggregation Recommendation Performance Communication Efficiency Low Rank Structure Federated Recommendation Low Rank Training

December 23, 2023

User Consented Federated Recommender System Against Personalized Attribute Inference Attack
Qi Hu, Yangqiu Song
Attribute Inference Attack Federated Recommendation

October 31, 2023

FedRec+: Enhancing Privacy and Addressing Heterogeneity in Federated Recommendation Systems
Lin Wang, Zhichao Wang, Xi Leng, Xiaoying Tang
Recommendation System Recommendation Performance Federated Recommendation Heterogeneity Challenge Protecting Privacy Near Optimal Virtual Rating

September 14, 2023

FedFNN: Faster Training Convergence Through Update Predictions in Federated Recommender Systems
Francesco Fabbri, Xianghang Liu, Jack R. McKenzie, Bartlomiej Twardowski, Tri Kurniawan Wijaya
Model Training Decentralized Federated Learning Federated Recommendation Training Convergence Accurate Training

July 25, 2023

GNN4FR: A Lossless GNN-based Federated Recommendation Framework
Guowei Wu, Weike Pan, Zhong Ming
Recommender System Recommendation Model Large Scale Graph Federated Recommendation Dfa GNN Lossless Performance Global Graph

May 9, 2023

FedPDD: A Privacy-preserving Double Distillation Framework for Cross-silo Federated Recommendation
Sheng Wan, Dashan Gao, Hanlin Gu, Daning Hu
Differential Privacy Distillation Framework Federated Recommendation Optimal Transport Distillation Cross Market Recommendation

April 17, 2023

DRIFT: A Federated Recommender System with Implicit Feedback on the Items
Theo Nommay
Recommender System Federated Setting Recommendation Quality Implicit Feedback Item Popularity Real Drift Federated Recommendation

January 22, 2023

Federated Recommendation with Additive Personalization
Zhiwei Li, Guodong Long, Tianyi Zhou
Recommendation System Federated Recommendation

January 16, 2023

Dual Personalization on Federated Recommendation
Chunxu Zhang, Guodong Long, Tianyi Zhou, Peng Yan, Zijian Zhang, Chengqi Zhang, Bo Yang
Privacy Preserving Federated Recommendation Dual Domain Anti Personalization

December 27, 2022

A Survey on Federated Recommendation Systems
Zehua Sun, Yonghui Xu, Yong Liu, Wei He, Lanju Kong, Fangzhao Wu, Yali Jiang, Lizhen Cui
Timely Survey Recommendation System Recommendation Model Empirical Privacy Privacy Constraint Federated Recommendation

Federated Recommendation

Papers

Co-clustering for Federated Recommender System

Efficient and Robust Regularized Federated Recommendation

P4GCN: Vertical Federated Social Recommendation with Privacy-Preserving Two-Party Graph Convolution Networks

Personalized Item Representations in Federated Multimodal Recommendation

Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach

Marking the Pace: A Blockchain-Enhanced Privacy-Traceable Strategy for Federated Recommender Systems

Navigating the Future of Federated Recommendation Systems with Foundation Models

Federated Recommendation via Hybrid Retrieval Augmented Generation

Poisoning Federated Recommender Systems with Fake Users

FedRKG: A Privacy-preserving Federated Recommendation Framework via Knowledge Graph Enhancement

Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Training

User Consented Federated Recommender System Against Personalized Attribute Inference Attack

FedRec+: Enhancing Privacy and Addressing Heterogeneity in Federated Recommendation Systems

FedFNN: Faster Training Convergence Through Update Predictions in Federated Recommender Systems

GNN4FR: A Lossless GNN-based Federated Recommendation Framework

FedPDD: A Privacy-preserving Double Distillation Framework for Cross-silo Federated Recommendation

DRIFT: A Federated Recommender System with Implicit Feedback on the Items

Federated Recommendation with Additive Personalization

Dual Personalization on Federated Recommendation

A Survey on Federated Recommendation Systems