# Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation

arXiv: https://arxiv.org/abs/2401.17630 · HTML: https://mlsanity.com/paper/2401.17630/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2024.

## Built on by (most cited first, 11 of 11)

- [A Comprehensive Data-centric Overview of Federated Graph Learning](https://mlsanity.com/paper/2507.16541/) (2025)
- [Graph Federated Learning for Personalized Privacy Recommendation](https://mlsanity.com/paper/2508.06208/) (2025)
- [Cloud-Device Collaborative Agents for Sequential Recommendation](https://mlsanity.com/paper/2509.01551/) (2025)
- [Brain Storm Optimization Based Swarm Learning for Diabetic Retinopathy Image Classification](https://mlsanity.com/paper/2404.15585/) (2024)
- [Watermarking Recommender Systems](https://mlsanity.com/paper/2407.21034/) (2024)
- [Far From Sight, Far From Mind: Inverse Distance Weighting for Graph Federated Recommendation](https://mlsanity.com/paper/2507.01285/) (2025)
- [A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2508.19620/) (2025)
- [AI-Generated Content in Cross-Domain Applications: Research Trends, Challenges and Propositions](https://mlsanity.com/paper/2509.11151/) (2025)
- [MDiffFR: Modality-Guided Diffusion Generation for Cold-start Items in Federated Recommendation](https://mlsanity.com/paper/2512.24715/) (2025)
- [Empowering Contrastive Federated Sequential Recommendation with LLMs](https://mlsanity.com/paper/2602.09306/) (2026)
- [Improving Federated Graph Recommendation with Semantic Guidance](https://mlsanity.com/paper/2606.15277/) (2026)

## Abstract (arXiv)

Federated recommender systems (FedRecs) have gained significant attention for their potential to protect user's privacy by keeping user privacy data locally and only communicating model parameters/gradients to the server. Nevertheless, the currently existing architecture of FedRecs assumes that all users have the same 0-privacy budget, i.e., they do not upload any data to the server, thus overlooking those users who are less concerned about privacy and are willing to upload data to get a better recommendation service. To bridge this gap, this paper explores a user-governed data contribution federated recommendation architecture where users are free to take control of whether they share data and the proportion of data they share to the server. To this end, this paper presents a cloud-device collaborative graph neural network federated recommendation model, named CDCGNNFed. It trains user-centric ego graphs locally, and high-order graphs based on user-shared data in the server in a collaborative manner via contrastive learning. Furthermore, a graph mending strategy is utilized to predict missing links in the graph on the server, thus leveraging the capabilities of graph neural networks over high-order graphs. Extensive experiments were conducted on two public datasets, and the results demonstrate the effectiveness of the proposed method.