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)
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Authors
- 1. Graham Cormode
- 2. Daniel Ting
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,540 | Frequency Estimation under Local Differential Privacy | 2021 | VLDB | 8.5714554e-05 |
| 2,556 | Answering Multi-Dimensional Analytical Queries under Local Differential Privacy | 2019 | SIGMOD | 8.5485513e-05 |
| 3,557 | Fast Manhattan Sketches in Data Streams | 2010 | PODS | 6.9711051e-05 |
| 7,855 | An Introduction to Federated Computation | 2022 | SIGMOD | 4.6305904e-05 |
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