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Paper Citation Record · LEDGER

Training Diffusion Models with Federated Learning

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2406.12575.

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

pith.paper-citation-record.v1
2406.12575 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 2026-08-09T06:31:02.800959+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:11:23.010440Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T00:07:28.676338Z

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 36e08c25-421f-41f0-8184-836ab0f97966 · inbound

CollaFuse: Collaborative Diffusion Models cites this paper.

CollaFuse: Collaborative Diffusion Models Training Diffusion Models with Federated Learning

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-23T23:35:52.228259Z

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=2026-05-23T23:35:00.048503Z digest=sha256:477a0511c932fa6365570cd3411696a3cd8820e6a8fb3b7c5029e67a20e21c9a

Observation 6d7f10f1-3200-488e-aec8-cf4e60aa6001 · inbound

FedPhD: Federated Pruning with Hierarchical Learning of Diffusion Models cites this paper.

FedPhD: Federated Pruning with Hierarchical Learning of Diffusion Models Training Diffusion Models with Federated Learning

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-06T19:11:23.010440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:11:23.010440Z digest=sha256:60c4911f99d881f5e037c1713ff50f22436bc030741c8e4ad9576f92aa01f430

Observation 8f2e12c0-ac8a-4863-be23-5d4127dd6dd9 · inbound

On the Tradeoffs of On-Device Generative Models in Federated Predictive Maintenance Systems cites this paper.

On the Tradeoffs of On-Device Generative Models in Federated Predictive Maintenance Systems Training Diffusion Models with Federated Learning

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-05-11T03:55:57.297875Z

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=2026-05-11T02:06:13.515696Z digest=sha256:112457ff9fe5864eb1d8f646ee15663f674b81b9b103db33040cec8eccbacda5

Observation 294de733-a76e-4093-b6f0-054cdd9a10da · inbound

UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment cites this paper.

UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment Training Diffusion Models with Federated Learning

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-12T02:51:18.054520Z

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=2026-05-12T02:47:38.972072Z digest=sha256:d254b097bb44b5c7d2ad34bc7715d83eeeb6a82b78a0bb6ab1efd18bac03e11e

Observation 51ce638f-24da-425a-8f5e-f730a6aa5aa6 · inbound

Compositional Generative Modeling from Decentralized Data cites this paper.

Compositional Generative Modeling from Decentralized Data Training Diffusion Models with Federated Learning

Reference 17

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T00:07:28.677990Z

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:d6b188c7bf7a5a5347bfb3f91c5a9c0d50b9cacc97a34cdb168525cafa02d88c