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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)

Paper ID
13888
Venue
VLDB
Year
2025
Pagerank
4.1945683e-05
Overall Rank
10,612 | 26.18%
DOI
10.14778/3742728.3742736

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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 8.5797299e-05
2,555 Answering Multi-Dimensional Analytical Queries under Local Differential Privacy 2019 SIGMOD 8.5477878e-05
3,566 Fast Manhattan Sketches in Data Streams 2010 PODS 6.9629443e-05
7,853 An Introduction to Federated Computation 2022 SIGMOD 4.6350359e-05
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