Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Safely Learning with Private Data: A Federated Learning Framework for Large Language Model
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2406.14898.
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 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T15:58:52.691602Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T16:06:29.811356Z
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 f9af3ea3-bede-43d6-b785-e051e927a789 · inbound
When IoT Meet LLMs: Applications and Challenges cites this paper.
When IoT Meet LLMs: Applications and Challenges Safely Learning with Private Data: A Federated Learning Framework for Large Language Model
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T15:58:52.691602Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d8a27910-f0f6-4e67-922e-a959fd8db579 · inbound
Feature Coding in the Era of Large Models: Dataset, Test Conditions, and Benchmark cites this paper.
Feature Coding in the Era of Large Models: Dataset, Test Conditions, and Benchmark Safely Learning with Private Data: A Federated Learning Framework for Large Language Model
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T21:35:18.613716Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8d788e7e-77b2-495f-9a1a-6f5e605b4b3f · inbound
Split and Aggregation Learning for Foundation Models Over Mobile Embodied AI Network (MEAN): A Comprehensive Survey cites this paper.
Split and Aggregation Learning for Foundation Models Over Mobile Embodied AI Network (MEAN): A Comprehensive Survey Safely Learning with Private Data: A Federated Learning Framework for Large Language Model
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T16:06:29.820420Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.