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Paper Citation Record · LEDGER

Precision-Weighted Federated Learning

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2107.09627.

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

pith.paper-citation-record.v1
2107.09627 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 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T10:52:19.220504Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-12T08:41:23.619921Z

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 223c24e6-0a81-416b-be6e-bcf3f39ba9af · inbound

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game cites this paper.

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game Precision-Weighted Federated Learning

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-11T10:52:19.220504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:52:19.220504Z digest=sha256:57bbe5e26014aa88a117414ecf5f592b54e3bc1a5cf436e7e05f4a9ae48c777b

Observation 37fae5e4-0dc2-44e8-8b19-faf6cc00dfc4 · inbound

Robust Server Defense Against Unreliable Clients in One-Shot Fair Collaborative Machine Learning cites this paper.

Robust Server Defense Against Unreliable Clients in One-Shot Fair Collaborative Machine Learning Precision-Weighted Federated Learning

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-05-12T08:41:23.622281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-12T00:52:25.736799Z digest=sha256:6dce288c1793a413d93bd82bc23c6653e0f92b5f8544df815ae8145d2fdb4c0f