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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)

Paper ID
7597
Venue
SIGMOD
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,387 | 28.74%
DOI
10.1145/3769811

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Authors

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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