# Federated Neural Collaborative Filtering

arXiv: https://arxiv.org/abs/2106.04405 · HTML: https://mlsanity.com/paper/2106.04405/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 69 references that are in the map.
- First posted on arXiv 2 Jun 2021.

## Builds on (most cited first, 23 of 23)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Exploiting Unintended Feature Leakage in Collaborative Learning](https://mlsanity.com/paper/1805.04049/) (2018)
- [Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints](https://mlsanity.com/paper/1910.01991/) (2019)
- [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Deep Learning based Recommender System: A Survey and New Perspectives](https://mlsanity.com/paper/1707.07435/) (2017)
- [Secure Federated Matrix Factorization](https://mlsanity.com/paper/1906.05108/) (2019)
- [Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System](https://mlsanity.com/paper/1901.09888/) (2019)
- [FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction](https://mlsanity.com/paper/2006.07273/) (2020)
- [Federating Recommendations Using Differentially Private Prototypes](https://mlsanity.com/paper/2003.00602/) (2020)
- [Privacy Threats Against Federated Matrix Factorization](https://mlsanity.com/paper/2007.01587/) (2020)

## Built on by (most cited first, 30 of 30)

- [A Survey on Federated Recommendation Systems](https://mlsanity.com/paper/2301.00767/) (2022)
- [Dual Personalization on Federated Recommendation](https://mlsanity.com/paper/2301.08143/) (2023)
- [Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity](https://mlsanity.com/paper/2206.02633/) (2022)
- [Federated Learning for 5G Base Station Traffic Forecasting](https://mlsanity.com/paper/2211.15220/) (2022)
- [Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Training](https://mlsanity.com/paper/2401.03748/) (2024)
- [Federated Recommendation with Additive Personalization](https://mlsanity.com/paper/2301.09109/) (2023)
- [Co-clustering for Federated Recommender System](https://mlsanity.com/paper/2411.01690/) (2024)
- [User Consented Federated Recommender System Against Personalized Attribute Inference Attack](https://mlsanity.com/paper/2312.16203/) (2023)
- [A Comprehensive Data-centric Overview of Federated Graph Learning](https://mlsanity.com/paper/2507.16541/) (2025)
- [A Privacy Preserving System for Movie Recommendations Using Federated Learning](https://mlsanity.com/paper/2303.04689/) (2023)
- [Cali3F: Calibrated Fast Fair Federated Recommendation System](https://mlsanity.com/paper/2205.13121/) (2022)
- [Towards Energy-Aware Federated Traffic Prediction for Cellular Networks](https://mlsanity.com/paper/2309.10645/) (2023)
- [A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation](https://mlsanity.com/paper/2403.03600/) (2024)
- [FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddings](https://mlsanity.com/paper/2210.13686/) (2022)
- [Preventing the Popular Item Embedding Based Attack in Federated Recommendations](https://mlsanity.com/paper/2502.12958/) (2025)
- [Cluster-Enhanced Federated Graph Neural Network for Recommendation](https://mlsanity.com/paper/2412.08066/) (2024)
- [RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulation](https://mlsanity.com/paper/2310.19163/) (2023)
- [UFGraphFR: Graph Federation Recommendation System based on User Text description features](https://mlsanity.com/paper/2501.08044/) (2025)
- [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)
- [Intelligent Client Selection for Federated Learning using Cellular Automata](https://mlsanity.com/paper/2310.00627/) (2023)
- [Privacy-Preserving Orthogonal Aggregation for Guaranteeing Gender Fairness in Federated Recommendation](https://mlsanity.com/paper/2411.19678/) (2024)
- [Task-Agnostic Federation over Decentralized Data: Research Landscape and Visions](https://mlsanity.com/paper/2503.03140/) (2025)
- [Far From Sight, Far From Mind: Inverse Distance Weighting for Graph Federated Recommendation](https://mlsanity.com/paper/2507.01285/) (2025)
- [Gradients as an Action: Towards Communication-Efficient Federated Recommender Systems via Adaptive Action Sharing](https://mlsanity.com/paper/2507.08842/) (2025)
- [A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2508.19620/) (2025)
- [MDiffFR: Modality-Guided Diffusion Generation for Cold-start Items in Federated Recommendation](https://mlsanity.com/paper/2512.24715/) (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)
- [Improving Federated Graph Recommendation with Semantic Guidance](https://mlsanity.com/paper/2606.15277/) (2026)

## Abstract (arXiv)

In this work, we present a federated version of the state-of-the-art Neural Collaborative Filtering (NCF) approach for item recommendations. The system, named FedNCF, enables learning without requiring users to disclose or transmit their raw data. Data localization preserves data privacy and complies with regulations such as the GDPR. Although federated learning enables model training without local data dissemination, the transmission of raw clients' updates raises additional privacy issues. To address this challenge, we incorporate a privacy-preserving aggregation method that satisfies the security requirements against an honest but curious entity. We argue theoretically and experimentally that existing aggregation algorithms are inconsistent with latent factor model updates. We propose an enhancement by decomposing the aggregation step into matrix factorization and neural network-based averaging. Experimental validation shows that FedNCF achieves comparable recommendation quality to the original NCF system, while our proposed aggregation leads to faster convergence compared to existing methods. We investigate the effectiveness of the federated recommender system and evaluate the privacy-preserving mechanism in terms of computational cost.