Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR)（山东大学-南洋理工大学人工智能国际联合研究院（C-FAIR））；Software School, Shandong University（山东大学软件学院）；Shandong University（山东大学）；Sino-Singapore International Joint Research Institute（中新国际联合研究院）；Alibaba-NTU Singapore Joint Research Institute（阿里巴巴-新加坡南洋理工大学联合研究学院）；Nanyang Technological University（南洋理工大学）；Microsoft Research Asia（微软亚洲研究院）
Federated learning has recently been applied to recommendation systems to protect user privacy. In federated learning settings, recommendation systems can train recommendation models only collecting the intermediate parameters instead of the real user data, which greatly enhances the user privacy. Beside, federated recommendation systems enable to collaborate with other data platforms to improve recommended model performance while meeting the regulation and privacy constraints. However, federated recommendation systems faces many new challenges such as privacy, security, heterogeneity and communication costs. While significant research has been conducted in these areas, gaps in the surveying literature still exist. In this survey, we-(1) summarize some common privacy mechanisms used in federated recommendation systems and discuss the advantages and limitations of each mechanism; (2) review some robust aggregation strategies and several novel attacks against security; (3) summarize some approaches to address heterogeneity and communication costs problems; (4)introduce some open source platforms that can be used to build federated recommendation systems; (5) present some prospective research directions in the future. This survey can guide researchers and practitioners understand the research progress in these areas.
Navigating the Future of Federated Recommendation Systems with Foundation Models
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University（香港理工大学数据科学与人工智能系）
A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation
Australian Artificial Intelligence Institute, University of Technology Sydney（澳大利亚人工智能研究所，悉尼技术大学）；College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University（香港理工大学数据科学与人工智能系）
Personalized Recommendation Models in Federated Settings: A Survey
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；Hong Kong University of Science and Technology（香港科技大学）；WeBank（微众银行）
Federated Latent Factor Model for Bias-Aware Recommendation with Privacy-Preserving
College of Computer and Information Science, Southwest University, Chongqing 400715, China（西南大学计算机与信息科学学院）；School of Engineering, University of Western Australia, Australia（西澳大学工程学院）；School of Cyber Science and Technology, Beihang University, Beijing 100191, China（北航信息科学与技术学院）
A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions
School of Software Engineering, Xi’an Jiaotong University（西安交通大学软件工程学院）；City, University of London（伦敦城市大学）；Ministry of Education Key Laboratory for Intelligent Networks and Network Security, the School of Information and Communication Engineering, and SMILES LAB, Xi’an Jiaotong University（教育部智能网络与网络 security key laboratory、信息与通信工程学院及SMILES实验室，西安交通大学）
FedFlex: Federated Learning for Diverse Netflix Recommendations
Vrije Universiteit Amsterdam Netherlands；Georgia Institute of Technology Mexico；Centrum Wiskunde \& Informatica Netherlands；Georgia Institute of Technology
A Survey on Personality-Aware Recommendation Systems
School of Computer and Communication Engineering, University of Science and Technology Beijing（北京科技大学计算机与通信工程学院）；Nanyang Technological University（南洋理工大学）
FedPOIRec: Privacy Preserving Federated POI Recommendation with Social Influence
Institute for Language and Speech Processing, Athena Research Center（雅典娜研究中心语言与语音处理研究所）；Department of Electrical and Computer Engineering, Democritus University of Thrace（色雷斯德谟克利特大学电气与计算机工程系）
A Systematic Survey on Federated Sequential Recommendation
School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；The Hong Kong Polytechnic University（香港理工大学）
Multimodal-enhanced Federated Recommendation: A Group-wise Fusion Approach
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University（吉林大学教育部符号计算与知识工程重点实验室）；PolyU Academy for Artificial Intelligence, Hong Kong Polytechnic University（香港理工大学PolyU人工智能学院）；Australian Artificial Intelligence Institute, FEIT, University of Technology Sydney（悉尼科技大学FEIT澳大利亚人工智能研究所）；College of Computer Science, Inner Mongolia University（内蒙古大学计算机学院）
