# A Survey on Federated Recommendation Systems

arXiv: https://arxiv.org/abs/2301.00767 · HTML: https://mlsanity.com/paper/2301.00767/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 55 of its 117 references that are in the map.
- First posted on arXiv 27 Dec 2022.

## Builds on (most cited first, 30 of 55)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [A Closer Look at Few-shot Classification](https://mlsanity.com/paper/1904.04232/) (2019)
- [Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training](https://mlsanity.com/paper/1712.01887/) (2017)
- [Hard Negative Mixing for Contrastive Learning](https://mlsanity.com/paper/2010.01028/) (2020)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [The Hidden Vulnerability of Distributed Learning in Byzantium](https://mlsanity.com/paper/1802.07927/) (2018)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [Can You Really Backdoor Federated Learning?](https://mlsanity.com/paper/1911.07963/) (2019)
- [Compressing Deep Convolutional Networks using Vector Quantization](https://mlsanity.com/paper/1412.6115/) (2014)
- [Privacy and Robustness in Federated Learning: Attacks and Defenses](https://mlsanity.com/paper/2012.06337/) (2020)
- [A Survey on Homomorphic Encryption Schemes: Theory and Implementation](https://mlsanity.com/paper/1704.03578/) (2017)
- [Federated Meta-Learning with Fast Convergence and Efficient Communication](https://mlsanity.com/paper/1802.07876/) (2018)
- [ATOMO: Communication-efficient Learning via Atomic Sparsification](https://mlsanity.com/paper/1806.04090/) (2018)
- [One-Shot Federated Learning](https://mlsanity.com/paper/1902.11175/) (2019)
- [BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning](https://mlsanity.com/paper/1905.06731/) (2019)
- [Secure Federated Matrix Factorization](https://mlsanity.com/paper/1906.05108/) (2019)
- [Data Poisoning Attacks on Factorization-Based Collaborative Filtering](https://mlsanity.com/paper/1608.08182/) (2016)
- [Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System](https://mlsanity.com/paper/1901.09888/) (2019)
- [Federated Reconstruction: Partially Local Federated Learning](https://mlsanity.com/paper/2102.03448/) (2021)
- [Practical One-Shot Federated Learning for Cross-Silo Setting](https://mlsanity.com/paper/2010.01017/) (2020)

## Built on by (most cited first, 25 of 25)

- [Poisoning Federated Recommender Systems with Fake Users](https://mlsanity.com/paper/2402.11637/) (2024)
- [Federated Recommendation with Additive Personalization](https://mlsanity.com/paper/2301.09109/) (2023)
- [Advancements in Federated Learning: Models, Methods, and Privacy](https://mlsanity.com/paper/2302.11466/) (2023)
- [FedDistill: Global Model Distillation for Local Model De-Biasing in Non-IID Federated Learning](https://mlsanity.com/paper/2404.09210/) (2024)
- [Efficient and Robust Regularized Federated Recommendation](https://mlsanity.com/paper/2411.01540/) (2024)
- [Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective](https://mlsanity.com/paper/2501.03301/) (2025)
- [A Systematic Survey on Federated Sequential Recommendation](https://mlsanity.com/paper/2504.05313/) (2025)
- [Federated Communication-Efficient Multi-Objective Optimization](https://mlsanity.com/paper/2410.16398/) (2024)
- [Diffusion Models in Recommendation Systems: A Survey](https://mlsanity.com/paper/2501.10548/) (2025)
- [Advocating for the Silent: Enhancing Federated Generalization for Non-Participating Clients](https://mlsanity.com/paper/2310.07171/) (2023)
- [RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulation](https://mlsanity.com/paper/2310.19163/) (2023)
- [Personalized Recommendation Models in Federated Settings: A Survey](https://mlsanity.com/paper/2504.07101/) (2025)
- [Federated Continual Recommendation](https://mlsanity.com/paper/2508.04792/) (2025)
- [Graph Federated Learning for Personalized Privacy Recommendation](https://mlsanity.com/paper/2508.06208/) (2025)
- [Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks](https://mlsanity.com/paper/2509.07499/) (2025)
- [FedRec+: Enhancing Privacy and Addressing Heterogeneity in Federated Recommendation Systems](https://mlsanity.com/paper/2310.20193/) (2023)
- [A Survey of Latent Factor Models in Recommender Systems](https://mlsanity.com/paper/2405.18068/) (2024)
- [Navigating the Future of Federated Recommendation Systems with Foundation Models](https://mlsanity.com/paper/2406.00004/) (2024)
- [Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels](https://mlsanity.com/paper/2412.00452/) (2024)
- [FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning](https://mlsanity.com/paper/2506.03777/) (2025)
- [A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation](https://mlsanity.com/paper/2506.11563/) (2025)
- [Far From Sight, Far From Mind: Inverse Distance Weighting for Graph Federated Recommendation](https://mlsanity.com/paper/2507.01285/) (2025)
- [A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2508.19620/) (2025)
- [Learning to Collaborate via Structures: Cluster-Guided Item Alignment for Federated Recommendation](https://mlsanity.com/paper/2602.21957/) (2026)
- [Sharpness-Aware Minimization for Generalized Embedding Learning in Federated Recommendation](https://mlsanity.com/paper/2603.11503/) (2026)

## Abstract (arXiv)

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.