P2 FedRec: Towards Privacy-Preserving and Personalized Federated Recommendation via Relationship Awareness
Summary: P2 FedRec: relationship-aware federated recommender that collaboratively builds user-relationship graphs and trains personalized local models. Provides multi-level privacy (data and edge) via embedding-shared local graphs and noisy global graph-guided aggregation, with theoretical guarantees and strong empirical gains. (summarized by gpt-5-mini on Feb 11 2026)
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Authors
- 1. Chenfei Hu (Beijing Institute of Technology)
- 2. Zihao Xu (Changchun University)
- 3. Tong Wu (University of Science and Technology Beijing)
- 4. You Li (Beijing Institute of Technology)
- 5. Chuan Zhang (Beijing Institute of Technology)
- 6. Liehuang Zhu (Beijing Institute of Technology)
BibTeX Citation
@inproceedings{hu_sigmod26,
title = {{P2 FedRec: Towards Privacy-Preserving and Personalized Federated Recommendation via Relationship Awareness}},
author = {Hu, Chenfei and Xu, Zihao and Wu, Tong and Li, You and Zhang, Chuan and Zhu, Liehuang},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3769811},
url = {https://dl.acm.org/doi/10.1145/3769811},
year = {2026}
}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,746 | Federated Heavy Hitter Analytics with Local Differential Privacy | 2025 | SIGMOD | 6.1017514e-05 |
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