Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models 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:2210.01708.
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 2026-08-03T20:29:50.497882Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T13:16:58.662496Z
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 78f028f8-0181-4564-963a-851ed1ac9c45 · inbound
ParaBlock: Communication-Computation Parallel Block Coordinate Federated Learning for Large Language Models cites this paper.
ParaBlock: Communication-Computation Parallel Block Coordinate Federated Learning for Large Language Models Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T20:29:50.497882Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4fdd7c54-21d1-4b7c-a8e5-adbe365ea40b · inbound
Amortizing Federated Adaptation: Hypernetwork Driven LoRA for Personalized Foundation Models cites this paper.
Amortizing Federated Adaptation: Hypernetwork Driven LoRA for Personalized Foundation Models Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning
Reference 70
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T13:16:58.664427Z
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.