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Paper Citation Record · LEDGER

FedDM: Enhancing Communication Efficiency and Handling Data Heterogeneity in Federated Diffusion Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2407.14730.

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

pith.paper-citation-record.v1
2407.14730 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 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-03T20:18:02.667339Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T20:18:55.680537Z

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 6878b453-2605-4978-9329-ec27afc75a7a · inbound

Compositional Generative Modeling from Decentralized Data cites this paper.

Compositional Generative Modeling from Decentralized Data FedDM: Enhancing Communication Efficiency and Handling Data Heterogeneity in Federated Diffusion Models

Reference 18

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T00:07:28.680490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-27T17:25:36.129471Z digest=sha256:9ece3356dbd84ce1858a65b4fba93d0a7d7867c45a2d8bea73614f5f79b184dd

Observation db335c73-8e62-4b42-92e7-c520720c4412 · inbound

Generative AI and Federated Learning for Intrusion Detection Systems: A Survey cites this paper.

Generative AI and Federated Learning for Intrusion Detection Systems: A Survey FedDM: Enhancing Communication Efficiency and Handling Data Heterogeneity in Federated Diffusion Models

Reference 167

Resolution
verified exact
arxiv_id, observed 2026-07-03T20:18:55.683437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-07-03T20:18:02.667339Z digest=sha256:0f475d2a9c828ae4677ee6cb6c3fd7811ef4593147569139e9d0e05444c46232