# Federated Latent Factor Model for Bias-Aware Recommendation with Privacy-Preserving

arXiv: https://arxiv.org/abs/2504.15090 · HTML: https://mlsanity.com/paper/2504.15090/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 84 references that are in the map.
- First posted on arXiv 21 Apr 2025.

## Builds on (most cited first, 8 of 8)

- [Bias and Debias in Recommender System: A Survey and Future Directions](https://mlsanity.com/paper/2010.03240/) (2020)
- [Secure Federated Matrix Factorization](https://mlsanity.com/paper/1906.05108/) (2019)
- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [Federated Learning for Internet of Things: Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2111.07494/) (2021)
- [Meta Matrix Factorization for Federated Rating Predictions](https://mlsanity.com/paper/1910.10086/) (2019)
- [A Review of Modern Fashion Recommender Systems](https://mlsanity.com/paper/2202.02757/) (2022)
- [Privacy Preserving Point-of-interest Recommendation Using Decentralized Matrix Factorization](https://mlsanity.com/paper/2003.05610/) (2020)
- [A Dynamic Linear Bias Incorporation Scheme for Nonnegative Latent Factor Analysis](https://mlsanity.com/paper/2309.10618/) (2023)

## Abstract (arXiv)

A recommender system (RS) aims to provide users with personalized item recommendations, enhancing their overall experience. Traditional RSs collect and process all user data on a central server. However, this centralized approach raises significant privacy concerns, as it increases the risk of data breaches and privacy leakages, which are becoming increasingly unacceptable to privacy-sensitive users. To address these privacy challenges, federated learning has been integrated into RSs, ensuring that user data remains secure. In centralized RSs, the issue of rating bias is effectively addressed by jointly analyzing all users' raw interaction data. However, this becomes a significant challenge in federated RSs, as raw data is no longer accessible due to privacy-preserving constraints. To overcome this problem, we propose a Federated Bias-Aware Latent Factor (FBALF) model. In FBALF, training bias is explicitly incorporated into every local model's loss function, allowing for the effective elimination of rating bias without compromising data privacy. Extensive experiments conducted on three real-world datasets demonstrate that FBALF achieves significantly higher recommendation accuracy compared to other state-of-the-art federated RSs.