# A Novel Privacy-Preserved Recommender System Framework based on Federated Learning

arXiv: https://arxiv.org/abs/2011.05614 · HTML: https://mlsanity.com/paper/2011.05614/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [A Survey on Federated Recommendation Systems](https://mlsanity.com/paper/2301.00767/) (2022)
- [A Payload Optimization Method for Federated Recommender Systems](https://mlsanity.com/paper/2107.13078/) (2021)
- [PPA: Preference Profiling Attack Against Federated Learning](https://mlsanity.com/paper/2202.04856/) (2022)
- [PEPPER: Empowering User-Centric Recommender Systems over Gossip Learning](https://mlsanity.com/paper/2208.05320/) (2022)
- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [Decaf: Data Distribution Decompose Attack against Federated Learning](https://mlsanity.com/paper/2405.15316/) (2024)

## Abstract (arXiv)

Recommender System (RS) is currently an effective way to solve information overload. To meet users' next click behavior, RS needs to collect users' personal information and behavior to achieve a comprehensive and profound user preference perception. However, these centrally collected data are privacy-sensitive, and any leakage may cause severe problems to both users and service providers. This paper proposed a novel privacy-preserved recommender system framework (PPRSF), through the application of federated learning paradigm, to enable the recommendation algorithm to be trained and carry out inference without centrally collecting users' private data. The PPRSF not only able to reduces the privacy leakage risk, satisfies legal and regulatory requirements but also allows various recommendation algorithms to be applied.