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Paper Citation Record · LEDGER

Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2305.11414.

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

pith.paper-citation-record.v1
2305.11414 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:14:51.846056Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T13:16:58.676882Z

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 e0c1be88-c442-47d7-a53f-5fa822d7fe47 · inbound

Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things cites this paper.

Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-07T14:14:51.846056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:14:51.846056Z digest=sha256:de14bb60b509290c40c3d93f4ebaca4129e0eb5bdb01aea37a0be16de717c354

Observation 934185bd-3d28-44e4-902e-bf3e1b2110da · inbound

A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation cites this paper.

A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models

Reference 64

Resolution
verified exact
arxiv_id, observed 2026-05-19T09:32:16.628423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-19T09:28:32.185398Z digest=sha256:d89f0ec8d12accb9db31191b8dd187c93febf076f9e81678ecd29e917d03f6a3

Observation a78a566f-48b2-4eb5-842a-fd1e75ccb2a9 · inbound

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation cites this paper.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-07T01:03:42.293293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T01:03:42.293293Z digest=sha256:069563ae40cf2b821463c99d4c85f9285f1d4b7e37d00eea775d468fa6c07e62

Observation 6882a275-b96b-492a-b12e-99218086745b · inbound

Personalized Federated Learning via Dual-Prompt Optimization and Cross Fusion cites this paper.

Personalized Federated Learning via Dual-Prompt Optimization and Cross Fusion Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T22:36:47.680139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:36:47.680139Z digest=sha256:b3f0f858d4460853c96c6b974204ae4b5c5d1f5b082cf1e124ed2bde909ae14a

Observation e8b1a959-0fe6-46db-9c69-61dc585d99d3 · inbound

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

FedPhD: Federated Pruning with Hierarchical Learning of Diffusion Models Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-06T19:11:23.069302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:11:23.069302Z digest=sha256:f2bdfb1f8a5dbc660e0368483a83f91fd02a869d8b94461fd0a73918f0c7f9d3

Observation 23075fa4-7062-43f8-9dc3-f1fb3956993c · inbound

Amortizing Federated Adaptation: Hypernetwork Driven LoRA for Personalized Foundation Models cites this paper.

Amortizing Federated Adaptation: Hypernetwork Driven LoRA for Personalized Foundation Models Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models

Reference 72

Resolution
verified exact
arxiv_id, observed 2026-07-02T13:16:58.678629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-28T01:27:04.241484Z digest=sha256:d62c43baa5e46d187d9d3faa70b953264b801d7584fa3a1a2254070fb4d58edb