Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methods
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2212.10025.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-23T17:08:05.240432Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T17:08:12.554625Z
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 b91d91db-3546-47af-8507-463a916f32b2 · inbound
Federated Co-tuning Framework for Large and Small Language Models cites this paper.
Federated Co-tuning Framework for Large and Small Language Models When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methods
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T17:08:12.557061Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 5eca485f-f0a1-4350-a30f-323dc5415eb1 · inbound
FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion cites this paper.
FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methods
Reference 41
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T12:56:06.089911Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.