Database Paper Browser

Back to papers

MinMax Sampling: A Near-optimal Global Summary for Aggregation in the Wide Area

Summary: MinMax Sampling enables fast, adaptive WAN-wide aggregation. MinMaxopt achieves optimal accuracy; MinMaxadp trades accuracy for speed/adaptivity, delivering ~8x higher accuracy across federated learning, distributed state, and hierarchical aggregation. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6470
Venue
SIGMOD
Year
2022
Pagerank
4.1945683e-05
Overall Rank
11,364 | 20.95%
DOI
10.1145/3514221.3526160

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank Citing Paper Year Venue Pagerank
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 11 of 11 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers