# FedeRank: User Controlled Feedback with Federated Recommender Systems

arXiv: https://arxiv.org/abs/2012.11328 · HTML: https://mlsanity.com/paper/2012.11328/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2020.

## Built on by (most cited first, 7 of 7)

- [A Survey on Federated Recommendation Systems](https://mlsanity.com/paper/2301.00767/) (2022)
- [Co-clustering for Federated Recommender System](https://mlsanity.com/paper/2411.01690/) (2024)
- [MC2-SF: Slow-Fast Learning for Mobile-Cloud Collaborative Recommendation](https://mlsanity.com/paper/2109.12314/) (2021)
- [FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddings](https://mlsanity.com/paper/2210.13686/) (2022)
- [Federated Unbiased Learning to Rank](https://mlsanity.com/paper/2105.04761/) (2021)
- [Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality](https://mlsanity.com/paper/2107.13876/) (2021)
- [FedFNN: Faster Training Convergence Through Update Predictions in Federated Recommender Systems](https://mlsanity.com/paper/2309.08635/) (2023)

## Abstract (arXiv)

Recommender systems have shown to be a successful representative of how data availability can ease our everyday digital life. However, data privacy is one of the most prominent concerns in the digital era. After several data breaches and privacy scandals, the users are now worried about sharing their data. In the last decade, Federated Learning has emerged as a new privacy-preserving distributed machine learning paradigm. It works by processing data on the user device without collecting data in a central repository. We present FedeRank (https://split.to/federank), a federated recommendation algorithm. The system learns a personal factorization model onto every device. The training of the model is a synchronous process between the central server and the federated clients. FedeRank takes care of computing recommendations in a distributed fashion and allows users to control the portion of data they want to share. By comparing with state-of-the-art algorithms, extensive experiments show the effectiveness of FedeRank in terms of recommendation accuracy, even with a small portion of shared user data. Further analysis of the recommendation lists' diversity and novelty guarantees the suitability of the algorithm in real production environments.