Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation
As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2103.11109.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T18:53:08.500908Z
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 5e228cf3-f471-4ae5-91af-f47de577741e · inbound
How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy cites this paper.
How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation
Reference 241
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T18:53:08.500908Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.