Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Randomized Quantization is All You Need for Differential Privacy in Federated Learning
As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2306.11913.
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 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-18T19:09:04.217591Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T19:11:46.626494Z
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 c8f3f913-1e9a-4ddf-96ba-78af662b5c17 · inbound
DPQuant: Efficient and Differentially-Private Model Training via Dynamic Quantization Scheduling cites this paper.
DPQuant: Efficient and Differentially-Private Model Training via Dynamic Quantization Scheduling Randomized Quantization is All You Need for Differential Privacy in Federated Learning
Reference 51
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T19:11:46.628918Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 4ce6f523-96aa-4dce-8c86-0fa34acf130c · inbound
Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacy cites this paper.
Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacy Randomized Quantization is All You Need for Differential Privacy in Federated Learning
Reference 35
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T20:41:09.453628Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.