Paper Citation Record · LEDGER

FedGAN: Federated Generative Adversarial Networks for Distributed Data

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2006.07228.

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

pith.paper-citation-record.v1
2006.07228 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:37:14.303082Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T08:59:43.087442Z

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 f7150485-f6e2-46dd-a909-7f152536d843 · inbound

FedCAR: Cross-client Adaptive Re-weighting for Generative Models in Federated Learning cites this paper.

FedCAR: Cross-client Adaptive Re-weighting for Generative Models in Federated Learning FedGAN: Federated Generative Adversarial Networks for Distributed Data

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:45.099458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b389b768-c0d6-414a-98c1-67c574bc5ab0 · inbound

GeFL: Model-Agnostic Federated Learning with Generative Models cites this paper.

GeFL: Model-Agnostic Federated Learning with Generative Models FedGAN: Federated Generative Adversarial Networks for Distributed Data

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T04:45:42.327217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ae1dac9-baa4-4033-bd1c-924a660e21e5 · inbound

Nested Annealed Training Scheme for Generative Adversarial Networks cites this paper.

Nested Annealed Training Scheme for Generative Adversarial Networks FedGAN: Federated Generative Adversarial Networks for Distributed Data

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-10T18:30:58.492134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97de9377-5886-4323-9207-cebafd3525bd · inbound

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead cites this paper.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead FedGAN: Federated Generative Adversarial Networks for Distributed Data

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-08T15:50:58.764361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e2b2ff1-93d2-4e4d-9253-67bd0f08cb6a · 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 FedGAN: Federated Generative Adversarial Networks for Distributed Data

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:41:23.613097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1586a0d3-1eb6-430b-8fcc-3a1ce3f32414 · inbound

FedGMI: Generative Model-Driven Federated Learning for Probabilistic Mixture Inference cites this paper.

FedGMI: Generative Model-Driven Federated Learning for Probabilistic Mixture Inference FedGAN: Federated Generative Adversarial Networks for Distributed Data

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:11:18.924047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 97ac5d8c-81a6-4159-af11-3c862a455685 · inbound

Fed-CausalDiff: Decoupled Synchronization for Federated Do-Simulation and Policy Evaluation cites this paper.

Fed-CausalDiff: Decoupled Synchronization for Federated Do-Simulation and Policy Evaluation FedGAN: Federated Generative Adversarial Networks for Distributed Data

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:59:43.089185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ee5911e9-2e76-419e-9cf3-5250d49d784f · inbound

Targeted Label-Flipping and Oversampling Attacks on Federated Conditional GANs cites this paper.

Targeted Label-Flipping and Oversampling Attacks on Federated Conditional GANs FedGAN: Federated Generative Adversarial Networks for Distributed Data

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T19:37:14.303082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.