Paper Citation Record · LEDGER

Federated Learning for Computer Vision

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2308.13558.

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

pith.paper-citation-record.v1
2308.13558 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:11:47.066887Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T22:56:19.682398Z

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 d9d30cc1-fbc4-446c-b10a-4d0e582b8a66 · inbound

Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models cites this paper.

Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models Federated Learning for Computer Vision

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T17:11:47.066887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1da5829-4ef2-474f-9641-3c0e4d1c5afc · inbound

FedNAMs: Performing Interpretability Analysis in Federated Learning Context cites this paper.

FedNAMs: Performing Interpretability Analysis in Federated Learning Context Federated Learning for Computer Vision

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T23:33:38.010222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1227e24-6703-4fc5-9323-339add08e054 · inbound

Reporte de vulnerabilidades en IIoT. Proyecto DEFENDER cites this paper.

Reporte de vulnerabilidades en IIoT. Proyecto DEFENDER Federated Learning for Computer Vision

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T17:27:13.496952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 092b091f-c593-4f6e-866d-71b3b901c11e · inbound

Portable Medical Imaging in Modern Healthcare: Fundamentals, AI-Based Taxonomy, Image Quality, and Open Challenges cites this paper.

Portable Medical Imaging in Modern Healthcare: Fundamentals, AI-Based Taxonomy, Image Quality, and Open Challenges Federated Learning for Computer Vision

Reference 159

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:35:18.834681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 95764491-c37c-4da1-9ed8-3502b748203b · inbound

Tiny Collaborative Inference for Occlusion-Robust Object Detection cites this paper.

Tiny Collaborative Inference for Occlusion-Robust Object Detection Federated Learning for Computer Vision

Reference 30

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T22:56:19.685275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.