Federated Data Distribution Shift Estimation
Summary: Introduces compact, privacy-aware sketches for estimating total-variation (L1) distance between data distributions in the federated model, scalable to many clients and supporting dynamic updates. Provides provable accuracy/privacy guarantees and practical experimental validation. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
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Authors
- 1. Graham Cormode (Meta; University of Warwick)
- 2. Daniel Ting (Meta)
BibTeX Citation
@article{cormode_vldb25,
title = {{Federated Data Distribution Shift Estimation}},
author = {Cormode, Graham and Ting, Daniel},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {8},
pages = {2399--2412},
doi = {10.14778/3742728.3742736},
url = {https://doi.org/10.14778/3742728.3742736},
year = {2025}
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,336 | Answering Multi-Dimensional Analytical Queries under Local Differential Privacy | 2019 | SIGMOD | 8.6206562e-05 |
| 3,203 | Frequency Estimation under Local Differential Privacy | 2021 | VLDB | 7.5406431e-05 |
| 4,446 | Fast Manhattan Sketches in Data Streams | 2010 | PODS | 6.5969248e-05 |
| 7,989 | An Introduction to Federated Computation | 2022 | SIGMOD | 5.4116268e-05 |
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