Paper Citation Record · LEDGER

Achieving Fairness Across Local and Global Models in Federated Learning

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2406.17102.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2406.17102 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:01:34.335827Z

measured 0 of 1 external citation measurements

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 bf62b874-a92e-4ffc-955d-659ef6cc6b56 · inbound

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning cites this paper.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Achieving Fairness Across Local and Global Models in Federated Learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T05:01:34.335827Z

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 2026-08-22T06:32:14.747728+00:00.