Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Achieving Fairness Across Local and Global Models in Federated Learning
As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2406.17102.
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 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T14:28:00.556024Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-28T19:52:35.676916Z
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 e22b1c0c-22d9-4fc9-bb66-f4c1ad7059b1 · inbound
A Post-Processing-Based Fair Federated Learning Framework cites this paper.
A Post-Processing-Based Fair Federated Learning Framework Achieving Fairness Across Local and Global Models in Federated Learning
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T14:28:00.556024Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b2ba7d95-968a-4f9a-886a-a778fa897fab · inbound
Fairness in Federated Learning: Fairness for Whom? cites this paper.
Fairness in Federated Learning: Fairness for Whom? Achieving Fairness Across Local and Global Models in Federated Learning
Reference 59
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:45:18.345055Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8e52ae2d-3781-4c5f-9701-13900d43cb71 · inbound
Demystifying the Optimal Fair Classifier in Multi-Class Classification cites this paper.
Demystifying the Optimal Fair Classifier in Multi-Class Classification Achieving Fairness Across Local and Global Models in Federated Learning
Reference 111
- Resolution
- verified exact
- arxiv_id, observed 2026-06-28T19:52:35.678885Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.