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)
Incoming Non-self Citations Over Time
Authors
- 1. Ergute Bao
- 2. Yizheng Zhu
- 3. Xiaokui Xiao
- 4. Yin Yang
- 5. Beng Chin Ooi
- 6. Benjamin Hong Meng Tan
- 7. Khin Mi Mi Aung
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,417 | DProvDB: Differentially Private Query Processing with Multi-Analyst Provenance | 2023 | SIGMOD | 4.7355114e-05 |
| 7,487 | Incentive-Aware Decentralized Data Collaboration | 2023 | SIGMOD | 4.7180617e-05 |
| 8,459 | Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs | 2024 | VLDB | 4.5065275e-05 |
| 10,664 | Calibrating Noise for Group Privacy in Subsampled Mechanisms | 2025 | VLDB | 4.1945683e-05 |
| 11,210 | FedCSS: Joint Client-and-Sample Selection for Hard Sample-Aware Noise-Robust Federated Learning | 2023 | SIGMOD | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,914 | Private Incremental Regression | 2017 | PODS | 4.8925595e-05 |
| 7,853 | An Introduction to Federated Computation | 2022 | SIGMOD | 4.6350359e-05 |
| 6,700 | Differentially Private Vertical Federated Clustering | 2023 | VLDB | 4.9563668e-05 |
| 5,775 | Federated Matrix Factorization with Privacy Guarantee | 2022 | VLDB | 5.3310992e-05 |
| 4,753 | Secure Shapley Value for Cross-Silo Federated Learning | 2023 | VLDB | 5.9469115e-05 |
| 6,459 | Practical Differentially Private and Byzantine-resilient Federated Learning | 2023 | SIGMOD | 5.0556005e-05 |
| 4,805 | Projected Federated Averaging with Heterogeneous Differential Privacy | 2022 | VLDB | 5.9102798e-05 |
| 11,043 | Uldp-FL: Federated Learning with Across-Silo User-Level Differential Privacy | 2024 | VLDB | 4.1945683e-05 |
| 7,797 | Quantifying identifiability to choose and audit epsilon in differentially private deep learning | 2021 | VLDB | 4.6482625e-05 |
| 10,664 | Calibrating Noise for Group Privacy in Subsampled Mechanisms | 2025 | VLDB | 4.1945683e-05 |