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Paper Citation Record · LEDGER

Achieving Fairness Across Local and Global Models in Federated Learning

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 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 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:45:18.345055Z

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 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.

source=arxiv_source observed=2026-08-07T13:45:18.345055Z digest=sha256:e054a58f3ca38a54322951817af60a77d20945619783bc7f909b09cf52a65ff3

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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-28T18:49:29.377237Z digest=sha256:ab6fc49da93e30a44198e7ce0bff5c231268fda86dd2c95f93a51350e0de4662