Paper Citation Record · LEDGER

Federated Learning via Synthetic Data

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2008.04489.

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

pith.paper-citation-record.v1
2008.04489 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:03:12.649374Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T22:23:47.906851Z

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 e2cdaa76-2be6-4c78-afb0-f0d98fe68f4f · inbound

Tackling Data Heterogeneity in Federated Time Series Forecasting cites this paper.

Tackling Data Heterogeneity in Federated Time Series Forecasting Federated Learning via Synthetic Data

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T14:03:12.649374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9be199d0-980b-4d32-911a-06c47a0ad7d7 · inbound

Exemplar-condensed Federated Class-incremental Learning cites this paper.

Exemplar-condensed Federated Class-incremental Learning Federated Learning via Synthetic Data

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T04:22:58.359031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce188022-932f-4aeb-be9b-1a6484a0aab1 · inbound

On Learning Representations for Tabular Data Distillation cites this paper.

On Learning Representations for Tabular Data Distillation Federated Learning via Synthetic Data

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T15:32:13.953341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2f3a10a-a9d6-4015-b01d-c02076735a68 · inbound

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing cites this paper.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Federated Learning via Synthetic Data

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T06:02:19.116812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fbe9aa4-ca10-4390-897a-8c052ee07b45 · inbound

FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios cites this paper.

FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Federated Learning via Synthetic Data

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T15:49:05.374401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33372c80-7a07-40a0-b082-268ddacca4b8 · inbound

Concordia: Self-Improving Synthetic Tables for Federated LLMs cites this paper.

Concordia: Self-Improving Synthetic Tables for Federated LLMs Federated Learning via Synthetic Data

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:16:27.558888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c87c9869-c8ab-47f6-b543-3fd6ad81ae65 · inbound

Concordia: Self-Improving Synthetic Tables for Federated LLMs cites this paper.

Concordia: Self-Improving Synthetic Tables for Federated LLMs Federated Learning via Synthetic Data

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-20T22:23:47.910624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a5242d1e-a088-4f5c-998a-93698f8ba5da · inbound

Dataset Distillation Based on Saliency-Driven Prototype Alignment cites this paper.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Federated Learning via Synthetic Data

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-01T02:50:18.983562Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e197044d-868e-497c-9c52-be79ad3d4535 · inbound

Dataset Distillation Based on Saliency-Driven Prototype Alignment cites this paper.

Dataset Distillation Based on Saliency-Driven Prototype Alignment Federated Learning via Synthetic Data

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:46.986353Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.