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Paper Citation Record · LEDGER

Asynchronous Federated Learning with non-convex client objective functions and heterogeneous dataset

As of 9 August 2026, this Paper Citation Record lists 2 of 2 outbound references and 0 inbound Pith citation observations for arXiv:2508.01675.

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

pith.paper-citation-record.v1
2508.01675 v1

Coverage vector

measured 2 of 2 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:28:38.325633Z

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

2 of 2 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved2
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 40144bb1-71fb-451f-ad4a-4b63f730c265 · outbound

This paper cites Evaluating Object Hallucination in Large Vision-Language Models.

Asynchronous Federated Learning with non-convex client objective functions and heterogeneous dataset Evaluating Object Hallucination in Large Vision-Language Models

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-06T05:28:38.220420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T05:28:38.220420Z digest=sha256:959b46e03e10346732809f2ab61ba56514b5ed1a5390e461edc819e5d67be50a

Observation e6954add-8b1d-4272-9f05-16a6c374c73a · outbound

This paper cites Fill the Gap: Quantifying and Reducing the Modality Gap in Image-Text Representation Learning.

Asynchronous Federated Learning with non-convex client objective functions and heterogeneous dataset Fill the Gap: Quantifying and Reducing the Modality Gap in Image-Text Representation Learning

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-06T05:28:38.325633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T05:28:38.325633Z digest=sha256:f9f1a450c3220cc6ab41c223bd2b3925c32142284172814672e58ae371fea9ba

Pith citing papers

No inbound Pith citation observations are available.