Uldp-FL: Federated Learning with Across-Silo User-Level Differential Privacy
Summary: Uldp-FL: first cross-silo FL framework to guarantee user-level DP when users' records span multiple silos via per-user weighted clipping instead of group-privacy. Provides formal privacy/utility analysis, a private weighting protocol, and improved DP–utility trade-offs. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Fumiyuki Kato (Kyoto University)
- 2. Li Xiong (Emory University)
- 3. Shun Takagi (Kyoto University)
- 4. Yang Cao (Tokyo Institute of Technology)
- 5. Masatoshi Yoshikawa (Osaka Seikei University)
BibTeX Citation
@article{kato_vldb24,
title = {{Uldp-FL: Federated Learning with Across-Silo User-Level Differential Privacy}},
author = {Kato, Fumiyuki and Xiong, Li and Takagi, Shun and Cao, Yang and Yoshikawa, Masatoshi},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {11},
pages = {2826--2839},
doi = {10.14778/3681954.3681966},
url = {https://doi.org/10.14778/3681954.3681966},
year = {2024}
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 62 | Privacy Integrated Queries: An Extensible Platform for Privacy-Preserving Data Analysis | 2009 | SIGMOD | 0.00038970535 |
| 8,489 | Network Shuffling: Privacy Amplification via Random Walks | 2022 | SIGMOD | 5.4152704e-05 |
| 11,438 | Olive: Oblivious Federated Learning on Trusted Execution Environment Against the Risk of Sparsification | 2023 | VLDB | 5.093636e-05 |
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