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Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy

Summary: Skellam Mixture Mechanism (SMM) for DP in federated learning with MPC reduces noise by using real-valued gradients via Skellam mixtures, while keeping updates confidential. Eliminates integer-gradient requirements, delivering stronger DP utility and robust empirical gains. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12915
Venue
VLDB
Year
2022
Pagerank
5.885424e-05
Overall Rank
6,406 | 56.05%
DOI
10.14778/3551793.3551798

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Authors

BibTeX Citation

@article{bao_vldb22,
        title = {{Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy}},
        author = {Bao, Ergute and Zhu, Yizheng and Xiao, Xiaokui and Yang, Yin and Ooi, Beng Chin and Tan, Benjamin Hong Meng and Aung, Khin Mi Mi},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {11},
        pages = {2348--2360},
        doi = {10.14778/3551793.3551798},
        url = {https://doi.org/10.14778/3551793.3551798},
        year = {2022}
}

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