# A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations

arXiv: https://arxiv.org/abs/2008.10808 · HTML: https://mlsanity.com/paper/2008.10808/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2020.

## Built on by (most cited first, 13 of 13)

- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [A Survey on Federated Recommendation Systems](https://mlsanity.com/paper/2301.00767/) (2022)
- [Federated Multi-View Learning for Private Medical Data Integration and Analysis](https://mlsanity.com/paper/2105.01603/) (2021)
- [Vertical Semi-Federated Learning for Efficient Online Advertising](https://mlsanity.com/paper/2209.15635/) (2022)
- [FedMood: Federated Learning on Mobile Health Data for Mood Detection](https://mlsanity.com/paper/2102.09342/) (2021)
- [Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources](https://mlsanity.com/paper/2305.06217/) (2023)
- [VFed-SSD: Towards Practical Vertical Federated Advertising](https://mlsanity.com/paper/2205.15987/) (2022)
- [Federated Deep Multi-View Clustering with Global Self-Supervision](https://mlsanity.com/paper/2309.13697/) (2023)
- [Federated Incomplete Multi-View Clustering with Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2406.08524/) (2024)
- [Personalized Recommendation Models in Federated Settings: A Survey](https://mlsanity.com/paper/2504.07101/) (2025)
- [Federated Learning Method for Preserving Privacy in Face Recognition System](https://mlsanity.com/paper/2403.05344/) (2024)
- [Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views](https://mlsanity.com/paper/2410.09484/) (2024)
- [A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2508.19620/) (2025)

## Abstract (arXiv)

Privacy-preserving recommendations are recently gaining momentum, since the decentralized user data is increasingly harder to collect, by recommendation service providers, due to the serious concerns over user privacy and data security. This situation is further exacerbated by the strict government regulations such as Europe's General Data Privacy Regulations(GDPR). Federated Learning(FL) is a newly developed privacy-preserving machine learning paradigm to bridge data repositories without compromising data security and privacy. Thus many federated recommendation(FedRec) algorithms have been proposed to realize personalized privacy-preserving recommendations. However, existing FedRec algorithms, mostly extended from traditional collaborative filtering(CF) method, cannot address cold-start problem well. In addition, their performance overhead w.r.t. model accuracy, trained in a federated setting, is often non-negligible comparing to centralized recommendations. This paper studies this issue and presents FL-MV-DSSM, a generic content-based federated multi-view recommendation framework that not only addresses the cold-start problem, but also significantly boosts the recommendation performance by learning a federated model from multiple data source for capturing richer user-level features. The new federated multi-view setting, proposed by FL-MV-DSSM, opens new usage models and brings in new security challenges to FL in recommendation scenarios. We prove the security guarantees of xxx, and empirical evaluations on FL-MV-DSSM and its variations with public datasets demonstrate its effectiveness. Our codes will be released if this paper is accepted.