Personalized Recommendation Models in Federated Settings: A Survey
Not on the map. Why?
Abstract (arXiv)
Federated recommender systems (FedRecSys) have emerged as a pivotal solution for privacy-aware recommendations, balancing growing demands for data security and personalized experiences. Current research efforts predominantly concentrate on adapting traditional recommendation architectures to federated environments, optimizing communication efficiency, and mitigating security vulnerabilities. However, user personalization modeling, which is essential for capturing heterogeneous preferences in this decentralized and non-IID data setting, remains underexplored. This survey addresses this gap by systematically exploring personalization in FedRecSys, charting its evolution from centralized paradigms to federated-specific innovations. We establish a foundational definition of personalization in a federated setting, emphasizing personalized models as a critical solution for capturing fine-grained user preferences. The work critically examines the technical hurdles of building personalized FedRecSys and synthesizes promising methodologies to meet these challenges. As the first consolidated study in this domain, this survey serves as both a technical reference and a catalyst for advancing personalized FedRecSys research.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 110 of its 222 references that are in the map. First posted on arXiv 10 Mar 2025.
Builds on
110 of 222 references.
References · 110
- 2023GPT-4 Technical Reportcited by 18,396
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,386
- 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
- 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
- 2019Advances and Open Problems in Federated Learningcited by 3,046
- 2018Federated Optimization in Heterogeneous Networkscited by 2,987
- 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
- 2017Neural Collaborative Filteringcited by 1,111
- 2021Model-Contrastive Federated Learningcited by 766
- 2019Graph Neural Networks for Social Recommendationcited by 714
38 more
- 2017Federated Multi-Task Learningcited by 672
- 2019Federated Learning with Personalization Layerscited by 541
- 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
- 2020Graph Neural Networks in Recommender Systems: A Surveycited by 494
- 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 459
- 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 383
- 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 374
- 2020Adaptive Personalized Federated Learningcited by 358
- 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 325
- 2020Three Approaches for Personalization with Applications to Federated Learningcited by 325
- 2020Self-supervised Graph Learning for Recommendationcited by 305
- 2020Bias and Debias in Recommender System: A Survey and Future Directionscited by 240
- 2024Foundation Models for Time Series Analysis: A Tutorial and Surveycited by 239
- 2021Personalized Federated Learning using Hypernetworkscited by 212
- 2018Explainable Recommendation: A Survey and New Perspectivescited by 210
- 2021FedKD: Communication Efficient Federated Learning via Knowledge Distillationcited by 187
- 2023UniAudio: An Audio Foundation Model Toward Universal Audio Generationcited by 127
- 2022FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clientscited by 125
- 2022Federated Learning with Partial Model Personalizationcited by 114
- 2021Parameterized Knowledge Transfer for Personalized Federated Learningcited by 109
- 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 108
- 2020Feature Inference Attack on Model Predictions in Vertical Federated Learningcited by 106
- 2019Secure Federated Matrix Factorizationcited by 102
- 2021Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendationcited by 101
- 2021FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendationcited by 98
- 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 90
- 2023When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directionscited by 84
- 2019Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation Systemcited by 79
- 2021Federated Reconstruction: Partially Local Federated Learningcited by 79
- 2021Contrastive Learning for Cold-Start Recommendationcited by 79
- 2020Interactive Path Reasoning on Graph for Conversational Recommendationcited by 72
- 2023LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendationcited by 72
- 2017On Sampling Strategies for Neural Network-based Collaborative Filteringcited by 71
- 2022Hypergraph Contrastive Collaborative Filteringcited by 63
- 2021Fairness in Rankings and Recommendations: An Overviewcited by 62
- 2020FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoringcited by 61
- 2021Federated Social Recommendation with Graph Neural Networkcited by 56
- 2024From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Modelscited by 53
50 most cited of 110 All 110 (JSON)
Built on by
Citing papers per year: 2025: 1.
1 paper.