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Paper Citation Record · LEDGER

Bridging the Gap Between Foundation Models and Heterogeneous Federated Learning

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2310.00247.

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

pith.paper-citation-record.v1
2310.00247 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:03:19.378853Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T20:10:18.152736Z

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 302f5050-0c74-4a47-a01d-e9e75d14c315 · inbound

Federated Large Language Models: Feasibility, Robustness, Security and Future Directions cites this paper.

Federated Large Language Models: Feasibility, Robustness, Security and Future Directions Bridging the Gap Between Foundation Models and Heterogeneous Federated Learning

Reference 134

Resolution
unresolved
no resolver link, observed 2026-08-15T22:03:19.378853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:03:19.378853Z digest=sha256:6b67aef15882d20667c0b283b1d60f5ea474765a2b6e7b0fd16c9055a6c049fa

Observation 74b1a860-ea5c-433f-8bda-c7ebcb41da4f · inbound

Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models cites this paper.

Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models Bridging the Gap Between Foundation Models and Heterogeneous Federated Learning

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-15T20:10:18.155916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-15T20:07:38.811639Z digest=sha256:4a5eb98d1b5dd7ee87ee3d311e84d5f3bb8d0fbb4a14d4a9ad2d3030ba47c98d

Observation 403d99ee-8fc8-4c0d-8e88-c5793100199a · inbound

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks cites this paper.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Bridging the Gap Between Foundation Models and Heterogeneous Federated Learning

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-14T04:16:48.821293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T04:16:48.821293Z digest=sha256:3c6317e480e40ba8c390381185dc531f9817df505308a01abb4e637b69947bef