Paper Citation Record · LEDGER

Advances and Open Challenges in Federated Foundation Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2404.15381.

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

pith.paper-citation-record.v1
2404.15381 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:36:18.067136Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T17:08:12.546375Z

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 002b0761-0cb4-4ad5-9c48-b7e3d39d4427 · inbound

Federated Co-tuning Framework for Large and Small Language Models cites this paper.

Federated Co-tuning Framework for Large and Small Language Models Advances and Open Challenges in Federated Foundation Models

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-23T17:08:12.548524Z

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 bb174c85-0152-415b-858b-21b2c781f771 · inbound

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation cites this paper.

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation Advances and Open Challenges in Federated Foundation Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:18.067136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2239d6d-7772-43fa-b35d-0921984cf48b · inbound

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models cites this paper.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models Advances and Open Challenges in Federated Foundation Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T23:19:32.941263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f288b9b4-4430-4848-8ddc-b77b93fc740a · inbound

Flexible Personalized Split Federated Learning for On-Device Fine-Tuning of Foundation Models cites this paper.

Flexible Personalized Split Federated Learning for On-Device Fine-Tuning of Foundation Models Advances and Open Challenges in Federated Foundation Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T20:33:20.544017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1938fa36-d99e-44ab-89e6-6e015a27a856 · inbound

Foundational Models and Federated Learning: Survey, Taxonomy, Challenges and Practical Insights cites this paper.

Foundational Models and Federated Learning: Survey, Taxonomy, Challenges and Practical Insights Advances and Open Challenges in Federated Foundation Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T05:37:33.861087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aaf6b9f5-6e9b-4b79-b0da-f7b6d15f4766 · inbound

FedSDR: Federated Self-Distillation with Rectification cites this paper.

FedSDR: Federated Self-Distillation with Rectification Advances and Open Challenges in Federated Foundation Models

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:18:16.272219Z

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 fbd93b91-143d-46f6-9140-a233f1fa222b · inbound

LAARA: Layer-Aware Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning cites this paper.

LAARA: Layer-Aware Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning Advances and Open Challenges in Federated Foundation Models

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-02T09:01:49.370964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.